Automatic transaction execution based on transaction log analysis

ABSTRACT

A device may receive a preauthorization associated with missed transaction prevention for a transaction account of a user, wherein the missed transaction prevention involves preventing a missed transaction associated with merchant accounts of the user. The device may monitor a transaction log of the transaction account and identify a transaction pattern associated with a merchant account based on a plurality of historical transactions identified in the transaction log related to the merchant account. The device may determine that a historical transaction of the plurality of historical transactions is not designated for automatic execution and that an execution of an upcoming transaction corresponding to the plurality of historical transactions is not scheduled. The device may cause an account transaction associated with the upcoming transaction to be automatically executed before a transaction period expiration, associated with the merchant account, passes.

BACKGROUND

Transactions associated with a merchant (e.g., a provider of goodsand/or services) may be executed regularly by a user. A regularlyoccurring transaction may be associated with a transaction period (e.g.,a time period in which the regularly occurring transaction must becompleted) set by the merchant associated with the regularly occurringtransaction. The regularly occurring transaction may be automaticallyexecuted (e.g., scheduled for execution before the transaction periodexpires) or the transaction may be executed manually by the user.

SUMMARY

According to some implementations, a method may include receiving, by adevice, a preauthorization associated with missed transaction preventionfor a transaction account of a user, wherein the missed transactionprevention involves preventing an occurrence of a missed transactionassociated with merchant accounts of the user; monitoring, by the deviceand based on the preauthorization, a transaction log of the transactionaccount; identifying, by the device, a transaction pattern associatedwith a merchant account, wherein the transaction pattern is identifiedbased on a plurality of historical transactions identified in thetransaction log being associated with the merchant account; determining,by the device and based on the transaction pattern, that a historicaltransaction of the plurality of historical transactions is notdesignated for automatic execution; determining, by the device and basedon determining that the historical transaction is not designated forautomatic execution, that an execution of an upcoming transactioncorresponding to the plurality of historical transactions is notscheduled; and causing, by the device, an account transaction associatedwith the upcoming transaction to be automatically executed before atransaction period expiration, that is associated with the merchantaccount, passes.

According to some implementations, a device may include one or morememories; and one or more processors, communicatively coupled to the oneor more memories, configured to: receive a preauthorization associatedwith missed transaction prevention for a transaction account of a user;monitor, based on the preauthorization, a transaction log of thetransaction account; identify a transaction pattern associated with amerchant account, wherein the transaction pattern is identified based ona plurality of historical transactions identified in the transaction logbeing associated with the merchant account; determine, based on acharacteristic of the merchant account, whether the merchant account isdesignated for automatic execution of transactions; and designate, basedon determining that the merchant account is not designated for automaticexecution of transactions, an account transaction to be automaticallyexecuted to prevent a missed transaction involving the merchant account.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions. The one or more instructions,when executed by one or more processors of a device, may cause the oneor more processors to: monitor, based on receiving a preauthorization, atransaction log of a transaction account of a user, wherein thepreauthorization is associated with missed transaction prevention forthe transaction account; identify a transaction pattern associated witha merchant account, wherein the transaction pattern is identified basedon a plurality of historical transactions identified in the transactionlog being associated with the merchant account; determine acharacteristic associated with an upcoming transaction that correspondsto the plurality of historical transactions; determine, based on thecharacteristic, whether an execution of the upcoming transaction isscheduled before a transaction period expiration; and designate, basedon determining that the execution of the upcoming transaction is notscheduled, an account transaction to be automatically executed beforethe transaction period expiration passes, wherein the accounttransaction corresponds to the upcoming transaction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C are diagrams of one or more example implementationsdescribed herein.

FIGS. 2 and 3 are diagrams of another one or more exampleimplementations described herein.

FIG. 4 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 5 is a diagram of example components of one or more devices of FIG.2 .

FIGS. 6-8 are flowcharts of example processes for automatic transactionexecution based on transaction log analysis.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A user of a user device may interact with a plurality of merchants(e.g., providers of goods and/or services). The user of the user devicemay complete recurring transactions (such as order transactions, billtransactions, mortgage transactions, lease transactions, and/or thelike) with a merchant regularly over time via the user device. Somemerchants may allow for the user to register for and/or authorizeautomatic execution of recurring transactions with the merchant (e.g.,scheduling transactions to automatically occur). However, certainmerchants may not provide the option for automatic execution ofrecurring transactions. Additionally, or alternatively, the user may notregister for and/or authorize the automatic execution of recurringtransactions.

When automatic execution of recurring transactions with a merchant isnot enabled, the user may need to perform a number of actions using theuser device to execute an upcoming transaction associated with therecurring transactions. For example, to execute the upcomingtransaction, the user may need to locate a merchant system (e.g., anapplication, a website, and/or the like) associated with the executionof the upcoming transaction, determine a transaction period (e.g., atime period in which the upcoming transaction must be completed),determine a transaction amount for the upcoming transaction, provideinformation associated with a transaction account (e.g., a bank account,an expense account, a credit account, and/or the like) associated withthe user to the merchant platform, and/or request or initiate atransaction from the transaction account to execute the upcomingtransaction. This may waste computing resources (e.g., processingresources, memory resources, communication resources, and/or the like)associated with the user device by requiring a number of additionalsteps to be performed on the user device to execute the upcomingtransaction associated with the recurring transactions.

When the user misses a transaction associated with the recurringtransactions (e.g., does not execute the upcoming transaction and/ordoes not execute the upcoming transaction within the transactionperiod), additional steps may be required to be performed on the userdevice. For example, the user may use the user device (or anotherdevice) to investigate, contest, and/or object to the missedtransaction. This may waste computing resources and/or network resourcesassociated with identifying the missed transaction, investigating themissed transaction, executing the missed transaction, and/or contactingthe merchant associated with the missed transaction. The merchant mayalso be negatively impacted and waste computing resources and/or networkresources associated with attempting to identify, detect, investigate,execute, and/or remedy the missed transaction.

Some implementations described herein enable a transaction analysisplatform to automatically execute transactions based on transaction loganalysis. The transaction analysis platform may run on or in combinationwith a user device. The transaction analysis platform may provide amissed transaction prevention service that receives a preauthorizationfrom the user device to access information associated with a transactionaccount (e.g., a bank account, an expense account, a credit account,and/or the like) of a user of the user device. The transaction analysisplatform may monitor a transaction log (e.g., a log of each transactionexecuted associated with the transaction account) of the transactionaccount and identify a transaction pattern (such as recurringtransactions) associated with a merchant account (e.g., an accountassociated with a merchant that provides goods and/or services). Thetransaction analysis platform may analyze a plurality of historicaltransactions (e.g., past transactions) within the transaction patternassociated with the merchant account to determine that the plurality ofhistorical transactions are not associated with automatic execution, todetermine a transaction period (e.g., a time period in which an upcomingtransaction associated with the historical transactions must becompleted), and/or to determine a transaction amount. The transactionanalysis platform may determine that the upcoming transaction associatedwith the historical transactions is not scheduled to be executed basedon the determination that the plurality of historical transactions arenot associated with automatic execution. The transaction analysisplatform may cause an account transaction (e.g., a transaction from thetransaction account of the user to the merchant account) associated withthe upcoming transaction to be automatically executed before thetransaction period passes.

As a result, the transaction analysis platform conserves computingresources of the user device and/or network resources that would havebeen otherwise used to locate a merchant system (e.g., an application, awebsite, and/or the like) associated with the execution of the upcomingtransaction, determine the transaction period, determine the transactionamount, provide information associated with the transaction accountassociated with the user to the merchant platform, and/or request orinitiate a transaction from the transaction account to complete theupcoming transaction. Additionally, some implementations describedherein may enable the transaction analysis platform to execute theupcoming transaction before the user misses the upcoming transaction(e.g., does not execute the upcoming transaction and/or does notexecuted the upcoming transaction within the transaction period). As aresult, the transaction analysis platform conserves computing resourcesof the user device and/or network resources that would have otherwisebeen used to identify the missed transaction, investigate the missedtransaction, execute the missed transaction, and/or contact the merchantassociated with the missed transaction. The merchant associated with themissed transaction may conserve computing resources and/or networkresources that would have otherwise been used to identify, detect,investigate, execute, and/or remedy the missed transaction.

FIGS. 1A-1C are diagrams of one or more example implementations 100described herein. As shown in FIGS. 1A-1C, a user device may beassociated with a transaction analysis platform, a transaction accountplatform, a merchant platform, a messaging platform, a transactionbackend system, and a merchant platform. A user of the user device mayinteract with the user device to register for a missed transactionprevention service with the transaction analysis platform. The missedtransaction prevention service may enable the transaction analysisplatform to automatically execute transactions based on an analysis of atransaction log of a transaction account associated with the user, ananalysis of historical transactions associated with a merchant, and/or adetermination that an upcoming transaction associated with thehistorical transactions is not designated for automatic executions. Insome implementations, one or more (or all) of the functions described asbeing performed by the transaction analysis platform may be performed bythe user device. In some implementations, one or more (or all) of thefunctions described as being performed by the transaction analysisplatform may be performed by collectively by the transaction analysisplatform and the user device.

As shown in FIG. 1A, and by reference number 110, the transactionanalysis platform may receive, from the user device, a preauthorizationassociated with the missed transaction prevention service that enablesthe transaction analysis platform to access information (e.g., thatpermits the transaction analysis platform to monitor a transactionaccount (e.g., a financial account, such as a bank account, a creditaccount, a debit account, and/or the like) associated with the user,and/or a merchant account (e.g., an account of the user associated witha merchant that provides goods and/or services) associated with theuser).

For example, the user device may be associated with the user, and theaccess information may include a set of credentials associated with anaccount of the user, such as a transaction account of the user, amerchant account of the user, a messaging account of the user, an onlineaccount (e.g., an internet browser account, a search engine account, asocial media account, a merchant account, an online shopping account,and/or the like) of the user, and/or the like. The set of credentialsmay include a username/password combination for the user and theaccount, a security token (e.g., that provides limited access to theaccount account) associated with the user and the account, a biometricassociated with the user, and/or the like.

As described herein, the transaction account may be associated with(e.g., registered to, available to, and/or the like) the user to permitthe user to engage in transactions via the transaction account (e.g.,using funds associated with the transaction account). The transactionaccount may be managed and/or maintained by the transaction accountplatform for the user (e.g., by using a transaction log to permit theuser to view and/or access transaction activity of the transactionaccount). In some implementations, the transaction account platform maymanage hundreds, thousands, or more transaction accounts associated witha plurality of users, where each of the transaction accounts may be usedin hundreds, thousands, or more transactions, and/or the like.

In some implementations, the merchant account may be associated with amerchant that provides goods and/or services. The user may be associatedwith a plurality of merchant accounts. The user may register for amerchant account using the merchant platform. The merchant account mayinclude terms, such as that the merchant agrees to provide a good and/orservice in exchange for the user completing a transaction of a setamount for each transaction period (e.g., a period in which thetransaction must be completed). For example, the merchant may provide asubscription-based service that provides a good and/or service. Thetransactions may be recurring transactions (e.g., the transactions mayoccur regularly over a time, such as weekly, monthly, semi-yearly,yearly, and/or the like), such as bill transactions, mortgagetransactions, lease transactions, subscription transactions, and/or thelike. The merchant may set a transaction period for the transactions.For example, a merchant may set a transaction period of one month (e.g.,the user must execute a transaction within a one-month period).

A failure to execute a transaction within the transaction period mayresult in a missed transaction. A missed transaction may result inpenalties from the merchant (such as additional fees added to a futuretransaction with the merchant, cancellation of a service provided by themerchant, recapture of a good provided by the merchant, and/or thelike). A missed transaction may result in a reduction of thecreditworthiness of the user (e.g., a lowering of a credit scoreassociated with the user).

In some implementations, the merchant may provide a payment plan (suchas a transaction amount per transaction period over a set number oftransaction periods). For example, if the price of a good and/or serviceprovided by a merchant is $10,000, the merchant providing the goodand/or service may offer a transaction amount of $1,000, and atransaction period of one month, such that the user would execute atransaction transferring $1,000 from a transaction account associatedwith the user to a transaction account associated with the merchant eachmonth for ten total months.

The merchant account may enable the user to provide transaction accountinformation associated with the user to the merchant platform to executetransactions for the goods and/or services provided by the merchant. Insome implementations, the merchant platform may enable the user toregister for an automatic transaction service associated with themerchant account of the user. The automatic transaction service mayautomatically execute transactions due from the user to the merchantbased on the transaction amount set by the merchant and the transactionperiod set by the merchant.

In some implementations, a messaging account may include an emailaccount, a text messaging account, an instant messaging account, a voicemessaging account, and/or the like. In some implementations, a messagingaccount may be associated with messages (e.g., stored on the userdevice, stored on a messaging platform utilized by the user of the userdevice, and/or the like), such as email messages, text messages, instantmessages, and/or the like. In some implementations, a messaging accountmay store hundreds, thousands, or more messages from hundreds,thousands, or more third parties, that include different types ofcontent (e.g., personal content, transaction-related content, recurringtransactions-related content, and/or the like). As described herein, themessaging account may receive transaction-related messages indicating anexecution status of a transaction. Such a transaction-related messagemay include information identifying a date of execution of thetransaction, an amount of the transaction, a merchant associated withthe transaction, an automation status (e.g., whether the transaction wasperformed automatically) of the transaction, and/or the like.

In some implementations, an online account may include an internetbrowser account, a search engine account, a social media account, amerchant account, an online shopping account, and/or the like. Theonline account may be associated with the online activity of the user ofthe user device. In some implementations, the online account may be usedfor merchant activity, such as signing up for a merchant account,visiting a merchant platform, and/or the like.

In some implementations, the transaction analysis platform may receivethe access information based on requesting the access information fromthe user device (e.g., by providing a prompt via a display associatedwith the user device), based on a user of the user device inputting theaccess information (e.g., via a user interface, via an applicationinstalled on the user device, and/or the like), and/or the like.According to some implementations, the transaction analysis platform mayperform a verification process to verify that a user that provided theinput is an authorized user of the user device and/or an authorized userassociated with an account (such as a transaction account and/or amerchant account) described herein. Such a verification process mayinclude requesting and processing credentials (e.g., a username,password, personal identification number, and/or the like) associatedwith an authorized user, personal information associated with anauthorized user, security information associated with an authorizeduser, biometric information associated with an authorized user, and/orthe like to authenticate the user. In some implementations, thetransaction analysis platform may utilize a two-factor authenticationprocess to receive authorization information from the user. Thetwo-factor authentication process may increase a security of providingthe transaction analysis platform with access to an account associatedwith the user by providing the transaction analysis platform withlimited access to the account, by providing the user of the user devicewith control over whether the transaction analysis platform can accessthe account, and/or the like.

The verification process and/or the two-factor authentication processadds an additional level of security to any action performed by thetransaction analysis platform and/or the user device. Verifying theidentity of the user before allowing access to an account associatedwith the user and/or executing a transaction associated with the missedtransaction prevention service allows for early identification offraudulent activity. Verifying the identity of the user allows thetransaction analysis platform to conserve user device computingresources that would have otherwise been used to perform thetransaction, identify the fraudulent activity, investigate thefraudulent activity, and/or report the fraudulent activity. The merchantassociated with the account and/or the transaction may also conservecomputing resources that would have otherwise been used to reverse thefraudulent activity for the user, and/or identify, detect, and diagnosethe fraudulent activity.

In some implementations, the access information may permit thetransaction analysis platform to access a browser and/or softwareapplication associated with the user (e.g., a client application on theuser device or another device associated with the user, a serverapplication serving a client application of the user device, and/or thelike) for monitoring online activity, social media activity, and/or thelike. In some implementations, the transaction analysis platform mayprompt the user of the user device to permit the transaction analysisplatform to access the browser and/or software application associatedwith the user. In some implementations, after receiving the accessinformation, the transaction analysis platform may access the browserand/or software application to monitor the user's online activity,social activity, and/or the like.

To maintain privacy of a user, the transaction analysis platform mayensure that the user opts in (e.g., via the preauthorization and/or theaccess information) to enable the transaction analysis platform toaccess the transaction account, to access to the merchant account, toaccess the messaging account, to monitor the transaction log associatedwith the transaction account, to monitor and/or access privateinformation of the user, and/or the like. Accordingly, the transactionanalysis platform may be configured to abide by any and all applicablelaws with respect to maintaining the privacy of the user and/or contentof the user's messaging account, transaction account, merchant account,and/or the like. In some implementations, the transaction analysisplatform may not download (or permanently store) any messages,transaction information, audio or image files or data, and/or the like,from the user device, the transaction analysis platform may anonymizeand/or encrypt any private information associated with the user and/oraccounts, messages, images, audio, and/or the like of the user, and/orthe like.

In some implementations, the transaction analysis platform may have ormay be configured to have limited access to the transaction account, themerchant account, the messaging account, images or audio associated withthe user, and/or the like. For example, the transaction analysisplatform may be configured to have access to the transaction accountperiodically and for a threshold time period and/or to a limited numberof most recently posted transactions (e.g., the last ten transactions,twenty transactions, and/or the like), to have access to a limitednumber of most recently received messages (e.g., the last ten messages,twenty messages, and/or the like), to have access to messages withcertain keywords or phrases (e.g., keywords or phrases representative ofa transaction, keywords or phrases representative of an automatictransaction, keywords or phrases representative of a recurringtransaction, and/or the like), to have access to a particular folder ofmessages (e.g., a specific inbox), and/or the like. According to someimplementations, the user may specify which information and/or the typesof information that the transaction analysis platform may have access toand/or receive.

In some implementations, the transaction analysis platform may receive,from the user device, a preauthorization associated with the missedtransaction prevention service that enables the user to configure thelevel of access and/or authorization provided to the transactionanalysis platform. For example, the preauthorization may authorize thetransaction analysis platform to execute any transactions that satisfy athreshold transaction amount set by the user. The preauthorization mayidentify specific merchant accounts and/or transaction accountsassociated with the user that the transaction analysis platform mayaccess. The preauthorization may allow the user to configure differentlevels of access and/or authorization for each merchant account and/ortransaction account provided by the user. For example, apreauthorization may enable the transaction analysis platform to executeany transaction associated with a first merchant, to execute anytransaction that satisfies a threshold transaction amount for a secondmerchant, to provide an alert to the user device of any transaction witha third merchant, and/or to not enable the transaction analysis platformto execute any transitions with a fourth merchant only if the userprovides additional confirmation.

As described herein, the user may provide access information associatedwith accessing an account associated with the user, enabling monitoringand/or analyzing of transaction information associated with usertransactions and/or the accounts, and/or executing transactionsassociated with the transaction account and/or the merchant account. Insome implementations, upon installing an application on the user device(e.g., an application for missed transaction prevention), theapplication may request (e.g., via an authentication token) that theuser authorize monitoring of the user's usage of the user device,characteristics of the user device, the user's transaction account,merchant account, and/or messaging account, and/or the like. Such arequest may indicate to the user that the monitoring is for analyzingtransaction-related activity of a user and executing an accounttransaction (e.g., a transaction from a transaction account associatedwith the user) to prevent a missed transaction. With an approvalauthorizing monitoring and/or analyzing of transaction informationassociated with user transactions, the application may monitor and/oranalyze transaction information to identify a transaction patternassociated with a merchant account, to determine that the transactionpattern is associated with an upcoming transaction, to determine thatthe upcoming transaction is not scheduled for automatic execution,and/or to cause an account transaction associated with the upcomingtransaction. In some implementations, the application may prompt theuser to authorize monitoring and/or analyzing transaction information toprevent a missed transaction, as described herein. In someimplementations, the application may not prompt the user to authorizemonitoring and/or analyzing transaction information until a particularevent has occurred (e.g., detection of a transaction, detection of atransaction pattern, detection of an upcoming transaction, receipt of aparticular type of message, and/or the like), or may prompt the user toconfirm a previous authorization when the particular event has occurred.The request and/or prompt may enable the user to opt out from beingmonitored by the application.

As further shown in FIG. 1A, and by reference number 120, thetransaction analysis platform may monitor transactions associated with atransaction account of the user. The transaction analysis platform maymonitor a transaction log, associated with the transaction account,provided by the transaction account platform. In some implementations,the transaction log may include every transaction executed that isassociated with the transaction account. In some implementations, thetransaction log may include a subset of the transactions executed thatis associated with the transaction account.

As further shown in FIG. 1A, and by reference number 130, thetransaction analysis platform may analyze the transaction log to detecta recurring transaction. The transaction analysis platform may detectrecurring transactions by identifying a transaction pattern associatedwith a merchant account within the transaction log.

The transaction log may include transaction information, such as atransaction identifier, a date of execution, a merchant accountidentifier associated with the transaction, a transaction amount, and/orthe like. The transaction analysis platform may identify the transactionpattern based on a plurality of historical transactions (e.g., pasttransactions included in the transaction log) that are associated with aspecific merchant account.

The transaction pattern may be based on one or more pattern identifiersidentified within the plurality of historical transactions, such as theplurality of historical transactions having the same transaction amountand/or a similar transaction amount (e.g., the differences intransaction amounts satisfy a threshold range), having a related date ofexecution and/or a similar date of execution (e.g., the date ofexecution occurs on the same and/or day each month, and/or thedifferences in date of executions satisfy a threshold range), having thesame period of execution between subsequent transactions of theplurality of historical transactions (e.g., each of the plurality ofhistorical transactions have the same and/or similar amount of timebetween date of executions, such one month between each of the pluralityof historical transactions), and/or the like.

For example, the transaction log may include four transactions (e.g.,historical transactions) associated with the merchant “ABC Co.” Thetransactions may be identified as transaction “0003,” “0025,” “0050,”and “0074.” Transaction 0003 may have a date of execution of Jun. 19,2019. Transaction 0025 may have a date of execution of Jul. 20, 2019.Transaction 0050 may have a date of execution of Aug. 22, 2019.Transaction 0074 may have a date of execution of Sep. 22, 2019. Each ofthe four transactions associated with ABC Co. may have the transactionamount of $39.99. The transaction analysis platform may identify atransaction pattern within these four transactions based on the fourtransactions having the same transaction amount ($39.99), having arelated date of execution (each transaction was executed on a similarday of the month), and/or having a similar period of execution betweeneach of the four transactions (each transaction occurred approximatelyone month apart).

The detection of recurring transactions may indicate that an upcomingtransaction associated with the plurality of historical transactions mayneed to be executed between the transaction account of the user and anaccount associated with the merchant. For example, based on the exampleabove, the transaction analysis platform may identify that an upcomingtransaction is associated with ABC Co, the upcoming transaction has atransaction amount of $39.99, and/or the upcoming transaction has atransaction period which expires on and/or near Oct. 22, 2019.

In some implementations, the transaction analysis platform maypre-process the transactions to reduce the quantity of transactions thatare further processed to detect recurring transactions, for example byidentifying a characteristic of the transaction that indicates recurringactivity, such as a description related to the transaction. Thecharacteristics may include a source of the transactions (e.g., based onan identifier of a source of the transactions), whether the source isincluded on a list of sources (e.g., identified merchants havingrecurring transactions), a time and/or date on which the transactionswere processed (e.g., designated and/or determined dates oftransactions), a type of the transaction, a value of the transaction,other information indicating a recurring transaction, and/or the like.Accordingly, the transaction analysis platform may not processtransactions to detect recurring transactions unless the transactionanalysis platform identifies a likelihood of recurring transactions. Inthis way, the transaction analysis platform may conserve computingresources and/or network resources that would have otherwise beenconsumed by processing transactions that could have been filtered out asirrelevant.

Additionally, or alternatively, the transaction analysis platform mayignore transactions (e.g., transactions involving merchants that are notlikely to be associated with recurring transactions, such astransactions that satisfy a threshold value, and/or the like). Forexample, the transaction analysis platform may identify transactionsassociated with a merchant that may be likely to be associated withrecurring transactions by performing a lookup of an identifierassociated with the merchant, by analyzing a repository of hundreds,thousands, or more transactions to determine if the merchant wasinvolved in a same type of transaction associated with other transactionaccounts of other users (e.g., indicating that the merchant may utilizerecurring transactions), and/or the like.

Additionally, or alternatively, the transaction analysis platform mayidentify one or more fields of entries of the transaction log associatedwith the transaction account and may ignore transactions with entriesthat include particular combinations of identifiers of entities,identifiers of sources, values of the transactions, dates of thetransactions, and/or the like. Such a technique conserves processingresources of the transaction analysis platform by reducing a quantity oftransactions that the transaction analysis platform processes, byfiltering out transactions that are unlikely to be recurringtransactions, and/or the like.

In some implementations, the transaction analysis platform may processthe transaction log using a combination of processing techniques (e.g.,after pre-processing the transactions) to identify transactions that maybe recurring. For example, the transaction analysis platform may processthe transaction log using a text processing technique (e.g., a naturallanguage processing technique, a text analysis technique, and/or thelike), a code processing technique, and/or the like. In someimplementations, the transaction analysis platform may process thetransaction log using an image processing technique (e.g., a computervision technique, an optical character recognition (OCR) technique,and/or the like to identify text corresponding to transactions of thetransaction log).

In some implementations, when processing the transaction log using thetext processing technique, the transaction analysis platform may processtext of entries in the transaction log to identify terms, phrases,and/or the like included in the text (e.g., to identify recurringtransactions included in the text, to extract information related to therecurring transactions, and/or the like). For example, the transactionanalysis platform may process the text of the transactions to identifyterms and/or phrases that may likely identify a recurring transaction.

In some implementations, when processing the transaction log using thecode processing technique, the transaction analysis platform may processcode associated with the transaction log to identify recurringtransactions included in the transaction log, to identify informationrelated to the recurring transactions, and/or the like. For example, thetransaction analysis platform may analyze code (e.g., hypertext markuplanguage (HTML) code, cascading style sheet (CSS) code, and/or the like)associated with the transaction log and/or transaction account platform,tags within the code (e.g., a div tag, an image tag, text-related tags,and/or the like) that are associated with the transactions, and/or thelike.

In some implementations, by processing the code, tags within the code,and/or the like, the transaction analysis platform may be capable ofidentifying text within the transaction log. For example, thetransaction analysis platform may be configured with information thatidentifies a hierarchy of the code associated with the transaction log(e.g., the code may be structured in a hierarchical manner that impactsexecution of the code, tags associated with the code may have ahierarchical structure to organize entries of the transaction log in thecode and/or to impact a manner in which the transaction log is providedfor display, and/or the like).

In some implementations, the transaction analysis platform may scan thehierarchical structure of the code associated with a transaction log toidentify recurring transactions, to identify information related to thetransaction log (entries corresponding to transactions in thetransaction log), and/or the like. For example, the transaction analysisplatform may scan the hierarchical structure of the code to identifyfields, entries, and/or text in the code. Continuing with the previousexample, if the transaction analysis platform identifies a field in thecode of the transaction, then the transaction analysis platform may scanthe hierarchical structure (e.g., tags that are at a higher or lowerlevel in the hierarchical structure) to identify information (e.g.,text, metadata, an entry, and/or the like) that may be associated withthe field. Continuing still with the previous example, the transactionanalysis platform may process the information associated with the fieldto determine whether the field is associated with recurring transactions(e.g., using a text processing technique to identify terms, phrases,values, and/or the like included in the information that indicates thatthe field is a field of an entry for recurring transactions).

Additionally, or alternatively, the transaction analysis platform may beconfigured to communicate with the merchant platform via automatedweb-based interactions (e. g., web scraping), where one or more scriptsmay be created and utilized to automatically visit the merchantplatform, input the access information to log into the user's merchantaccount, click various buttons and/or links on the website, and/or thelike, to obtain the information related to the merchant account (such asbilling information, transaction amount information, transaction periodinformation, transaction due date information, and/or the like). Therecurring transactions may be identified based on the informationobtained via the automated web-based interactions.

As shown in FIG. 1B, and by reference number 140, the transactionanalysis platform may obtain information associated with a transactionexecution setting of the detected recurring transactions associated witha merchant account. The transaction execution setting may be related towhether the detected recurring transactions associated with the merchantaccount are designated for automatic execution (e.g., set toautomatically execute each transaction period). In some implementations,the transaction analysis platform may obtain the information associatedwith the transaction execution setting based on detecting the recurringtransactions associated with a merchant account, a date of execution ofthe recurring transactions, a setting associated with the merchantplatform, and/or the like.

As shown by reference number 140 a, the transaction analysis platformmay receive online activity from the user device associated with theuser. The online activity may be associated with an online account ofthe user, as described above. For example, the transaction analysisplatform may be configured to monitor online activity of the user, suchas the user accessing webpages using a browser (e.g., on the userdevice) to conduct merchant activity (e.g., execute transactions on amerchant platform, sign up for a merchant account, access informationrelated to a merchant account on the merchant platform, accessinformation related to a transaction account, and/or the like) with amerchant platform. Such online activity and/or similar online activity(e.g., social media activity, searches, sending messages, accessingmedia, accessing merchant platforms, and/or the like) involving merchantactivity may indicate that the user has recurring transactionsassociated with a merchant account, is manually executing recurringtransactions associated with a merchant account, is not visiting amerchant platform on a date of execution of one or more of the recurringtransactions (e.g., indicating that the transaction was executedautomatically), and/or the like. Similarly, in some implementations,other activity can be monitored (e.g., sending a message identifyingrecurring transaction information, accessing offline media associatedwith the recurring transaction information, and/or the like) todetermine the execution setting associated with the recurringtransactions.

In some implementations, online activity may include informationobtained via automated web-based interactions (e. g., web scraping), asdescribed above. After receiving preauthorization to access a merchantaccount, as described above, the transaction analysis platform mayutilize automated web-based interactions to visit a merchant platformassociated with a merchant, navigate the platform to access merchantaccount information associated with the user, and/or determine, from themerchant account information associated with the user, the executionsetting associated with the merchant account.

In some implementations, the online activity may be monitored on theuser device associated with the user. For example, the online activitymay be monitored via an application running on the user device and/or anapplication (e.g., an applet, an application programming interface, aplug-in, a browser extension, and/or the like) installed on a browser ofthe user device. The online activity may be monitored using any suitabletechniques, such as scraping hypertext markup language (HTML) associatedwith the online activity, capturing search strings associated with theonline activity, and/or the like.

In some implementations, the user device may conduct merchant activity(such as execute a transaction, register for a merchant accountassociated with the user, purchase a good and/or service provided by themerchant associated with the merchant platform, and/or the like) withthe merchant platform. In some implementations, the merchant platformmay provide transaction information (e.g., recurring transaction relatedinformation, such as a transaction amount, a transaction date ofexecution, an upcoming transaction due date, an execution settingrelated to the recurring transactions, an execution period relating tothe recurring transactions, and/or the like) to the messaging platformin the form of a message, as described below. Additionally, oralternatively, the merchant platform may provide the transactioninformation directly to the transaction analysis platform.

As further shown in FIG. 1B, and by reference number 140 b, thetransaction analysis platform may analyze messages to identify recurringtransaction information, such as the execution setting of recurringtransactions associated with a merchant account. As shown, thetransaction analysis platform may access a messaging account (e.g., amessaging account maintained by the messaging platform) of the user andprocess the messages in the messaging account (e.g., messages that aremaintained and/or stored by the messaging platform). For example, thetransaction analysis platform may process the messages to identifyrecurring transaction information. As used herein, recurring transactioninformation may include one or more keywords indicating an executionsetting of a transaction (such as automatic, auto-pay, regular,predetermined, scheduled, and/or the like), information relating to adate of execution associated with recurring transactions, informationrelating to an execution amount associated with recurring transactions,information relating to an execution period associated with recurringtransactions, and/or the like. For example, the recurring transactioninformation may include transaction confirmation messages from amerchant, inquiries sent to a merchant relating to a merchant accountassociated with the user, and/or the like.

As used herein, a message that includes recurring transactioninformation is referred to as a “recurring transaction related message.”In some implementations, the transaction analysis platform may processhundreds, thousands, or more messages in hundreds, thousands, or moremessaging accounts associated with hundreds, thousands, or more users.Accordingly, the transaction analysis platform may perform one or morerigorous computerized processes to process the messages of the messagingaccount.

In some implementations, the transaction analysis platform maypre-process the messages to reduce the quantity of messages that arefurther processed to identify recurring transaction related messages,for example by identifying a characteristic of the message that suggestsa message related to recurring transactions. The characteristic maycorrespond to a source of the message (e.g., domain name of a source ofthe message, whether the source is included on a list of sources (e.g.,a list of merchant platforms and/or the like), and/or the like), afolder into which the message has been filtered by the messagingplatform (e.g., an inbox, a promotions folder, a spam folder, acustomized folder, and/or the like), a time and/or date on which themessage was received, and/or the like. Accordingly, the transactionanalysis platform may not process messages to identify a recurringtransaction related message unless the transaction analysis platformidentifies a likelihood that the messages include recurring transactionrelated information. In this way, the transaction analysis platform mayconserve computing resources and/or network resources that may haveotherwise been consumed by processing messages that are not related torecurring transactions and that could have been filtered out asirrelevant.

Additionally, or alternatively, the transaction analysis platform mayignore messages (e.g., from third parties that are not likely to beassociated with recurring transaction related information, such aspersonal messages, newsletters, and/or the like). For example, thetransaction analysis platform may identify messages associated with athird party that may be likely to be associated with a recurringtransaction related message by performing a lookup of a domain nameassociated with a message, by analyzing a repository of hundreds,thousands, or more messages to determine if the same type of message wassent to other messaging accounts (e.g., indicating that the message maybe related to recurring transactions), by performing a lookup of aportion of a source identifier (e.g., a user identifier before the “@”symbol in an email address), and/or the like. Additionally, oralternatively, and as another example, the transaction analysis platformmay analyze historical messages associated with a folder and determine atheme for the folder and may ignore messages in that folder (e.g., afolder with a personal theme, a promotional theme, and/or the like)and/or process the messages in that folder (e.g., a recurringtransaction-related folder, a bill paying folder, a transaction folder,a receipt folder, and/or the like) based on the theme.

Additionally, or alternatively, the transaction analysis platform mayidentify terms and/or phrases included in a subject line and/or in abody of a message and may ignore messages that include particularcombinations of terms and/or phrases. Additionally, or alternatively,and as another example, the transaction analysis platform may ignoreduplicate messages (e.g., messages that have the same header, the samebody, the same unique identifier, and/or the like). Such a techniqueconserves processing resources of the transaction analysis platform byreducing a quantity of messages that the transaction analysis platformmay be required to process, by filtering messages that are unlikely tobe recurring transaction related messages, and/or the like.

In some implementations, the transaction analysis platform may processthe messages using a combination of processing techniques (e.g., afterpre-processing the messages) to identify messages that may be recurringtransaction related messages. For example, the transaction analysisplatform may process the messages using an image processing technique(e.g., a computer vision technique, an optical character recognition(OCR) technique, etc.), a text processing technique (e.g., a naturallanguage processing technique, a text analysis technique, etc.), a codeprocessing technique, and/or the like.

In some implementations, when processing the messages using the imageprocessing technique, the transaction analysis platform may processimages associated with the messages. In some implementations, thetransaction analysis platform may identify that a message includes animage by detecting that the message includes an image as an attachment(e.g., based on a file type of the attachment), by detecting an image ina body of the message (e.g., as compared to detecting text in the bodyof the message), by processing code associated with the message todetect an image (e.g., by detecting an image tag in code of an email orby detecting a unique resource identifier for an image included in thecode), and/or the like. In some implementations, the transactionanalysis platform may process the image to identify a term, a phrase, alogo, a symbol, and/or the like included in the image. For example, thetransaction analysis platform may process the image using OCR toidentify recurring transaction related information included in theimage, a merchant associated with the recurring transaction relatedinformation, and/or the like.

Additionally, or alternatively, the transaction analysis platform maycapture an image of the body of a message (e.g., text and imagesincluded in the body of the message), such as by saving a copy of themessage as a portable data format (PDF) document or by capturing animage of the message, and may process the image in a similar manner(e.g., to identify terms, phrases, logos, and/or the like included inthe image of the body of the message). For example, the transactionanalysis platform may capture an image of text of the message, imagesincluded in the message, and/or the like and may process the image toidentify recurring transaction related information included in themessage, to extract information related to the recurring transactionsfrom the message, and/or the like.

In some implementations, when processing the messages using the textprocessing technique, the transaction analysis platform may process textof the messages to identify terms, phrases, and/or the like included inthe text (e.g., to identify recurring transactions included in the text,to extract information related to the recurring transactions, and/or thelike). For example, the transaction analysis platform may process thetext of the messages to identify terms, phrases, and/or context (e.g.,using natural language processing) that may likely be related torecurring transaction related information, that identify a merchant(e.g., a merchant associated with the recurring transactions) with whichrecurring transactions are likely to be associated, and/or the like.

In some implementations, when processing the messages using the codeprocessing technique, the transaction analysis platform may process codeassociated with the messages to identify recurring transaction relatedinformation included in the messages, to identify information related tothe recurring transaction related information, and/or the like. Forexample, the transaction analysis platform may analyze code (e.g.,hypertext markup language (HTML) code, cascading style sheet (CSS) code,and/or the like) associated with messages, tags within the code (e.g., adiv tag, an image tag, text-related tags, and/or the like) that areassociated with the messages, and/or the like.

In some implementations, by processing the code, tags within the code,and/or the like, the transaction analysis platform may be capable ofidentifying text within a message, images within the message, and/or thelike that indicate a likelihood that the message is associated withrecurring transaction related information. For example, the transactionanalysis platform may be configured with information that identifies ahierarchy of the code associated with the message (e.g., the code may bestructured in a hierarchical manner that impacts execution of the code,tags associated with the code may have a hierarchical structure toorganize information in the code and/or to impact a manner in which theinformation is provided for display, and/or the like). In someimplementations, the transaction analysis platform may scan thehierarchical structure of the code associated with a message to identifyrecurring transaction related information included in the message, toidentify information related to the message, and/or the like. Forexample, the transaction analysis platform may scan the hierarchicalstructure of the code to identify text and/or images included in thecode. Continuing with the previous example, if the transaction analysisplatform identifies an image in the code of the message, then thetransaction analysis platform may scan the hierarchical structure (e.g.,tags that are at a higher or lower level in the hierarchical structure)to identify information (e.g., text, metadata, etc.) that may beassociated with the image. Continuing still with the previous example,the transaction analysis platform may process the information associatedwith the image to determine whether the image includes recurringtransaction related information (e.g., using a text processing techniqueto identify terms, phrases, and/or the like included in the informationthat indicates that the image includes recurring transaction relatedinformation).

In some implementations, the transaction analysis platform may obtainone or more template recurring transaction related messages that areassociated with one or more merchants known to send recurringtransaction related messages. The transaction analysis platform maystore the one or more template recurring transaction related messages ina data structure to enable the transaction analysis platform to use theone or more template recurring transaction related messages to processthe messages of the messaging account. Accordingly, the transactionanalysis platform may obtain, from the data structure, the one or moretemplate recurring transaction related messages and use the one or moretemplate recurring transaction related messages to process the messagesof the messaging account (e.g., by ignoring messages that do not followthe template(s) and/or recognizing recurring transaction relatedinformation in messages that do follow the template(s)).

Accordingly, the transaction analysis platform may train a messageanalysis model based on one or more parameters associated withidentifying recurring transaction related information in one or moremessages, such as a format of a recurring transaction related message, atemplate of a recurring transaction related message, an image associatedwith a recurring transaction related message, a field (e.g., a datefield, a time field, an execution amount field, an execution settingfield, and/or the like) associated with a recurring transaction relatedmessage, a type of a recurring transaction related message (e.g., anotification message, a confirmation message, a receipt, and/or thelike), a merchant associated with a recurring transaction relatedmessage, an attachment associated with a recurring transaction relatedmessage, and/or the like. The transaction analysis platform may trainthe message analysis model using historical data associated withidentifying recurring transaction related information within messagesaccording to the one or more parameters. Using the historical data andthe one or more parameters as inputs to the message analysis model, thetransaction analysis platform may determine that a message is arecurring transaction related message or that a message is not arecurring transaction related message in order to determine whether amessaging account includes a recurring transaction related messageassociated with recurring transaction related information. The messageanalysis model may be one or more machine learning models trained toidentify recurring transaction related information in one or moremessages. The one or more machine learning models may be trained and/orused in a manner similar to that described below with respect to FIGS. 2and 3 .

As further shown in FIG. 1B, and by reference number 140 c, thetransaction analysis platform may receive transaction specificinformation from the transaction account platform. The transactionspecific information may include the information contained within atransaction log associated with a transaction account of a user, asdescribed above. The transaction specific information may includeinformation relating to historical transactions. The transactionspecific information may include transaction amounts of transactionsincluded in the transaction specific information, informationidentifying merchants associated with transactions included in thetransaction specific information, information identifying dates ofexecution of transactions included in the transaction specificinformation, and/or the like.

In some implementations, the transaction account platform may providethe transaction specific information in real time (e.g., the transactionaccount platform may provide transaction specific information to thetransaction analysis platform as the transactions are executed). In someimplementations, the transaction account platform may provide updatedtransaction specific information when prompted by the user. In someimplementations, the transaction account platform may provide thetransaction specific information periodically and/or in batches.

In some implementations, the transaction specific information mayinclude a transaction account setting related to the transactionspecific information. The transaction account setting may be whether oneor more historical transactions are designated for automatic execution(such as automatic bill pay and/or the like), whether all historicaltransactions (and/or future transactions) associated with a merchant aredesignated for automatic execution, and/or the like. The transactionaccount setting may be received and/or stored by the transaction accountplatform. The transaction analysis platform may receive the transactionaccount setting from the transaction account platform. Additionally, oralternatively, the transaction analysis platform may receive thetransaction account setting from the user device. If the transactionanalysis platform identifies that a transaction account settingindicates that one or more historical transactions are designated forautomatic execution, the transaction analysis platform may not performadditional analysis related to determining whether the one or morehistorical transactions are designated for automatic execution (such asanalysis described with respect to FIG. 1C). As such, the transactionanalysis platform may pre-process historical transactions prior todetermining whether the historical transactions are designated forautomatic execution. In this way, the transaction analysis platform mayconserve computing resources and/or network resources that would haveotherwise been used to determine if the one or more historicaltransactions are designated for automatic execution.

The transaction analysis platform may obtain information associated withthe transaction execution setting of recurring transactions. Thetransaction analysis platform may use online activity associated withthe user device of the user, recurring transaction informationassociated with a merchant account, message analysis of messagesreceived by the messaging platform, transaction specific informationreceived from the transaction account platform, and/or the like todetermine the execution setting of the recurring transactions. In thisway, the transaction analysis platform may gather a plurality ofrecurring transaction related information associated with the user todetermine the execution setting of the recurring transactions.

As shown in FIG. 1C, and by reference number 150 a, the transactionanalysis platform may determine that the recurring transactions have notbeen designated to be automatically executed. The transaction analysisplatform may determine that the execution setting of the recurringtransactions is not automatic based on the information obtained asdescribed above with respect to FIG. 1B. In some implementations, thetransaction analysis platform may determine that a historicaltransaction of the plurality of historical transactions associated withthe recurring transactions was not designated for automatic execution.

Typically, recurring transactions designated for automatic executionhave the same and/or related execution amounts, dates of execution,and/or periods of execution between each of the transactions of theplurality of historical transactions. The transaction analysis platformmay determine that the recurring transactions have not beenautomatically executed based on the transaction pattern identified, asdescribed above. For example, the transaction analysis platform maydetermine that the plurality of historical transactions do not haverelated dates of execution (e.g., the dates of execution for each of theplurality of historical transactions are all on different days of themonth), do not have similar transaction amounts, and/or do not haverelated periods of execution between each of the transactions of theplurality of historical transactions. The transaction analysis platformmay designate a threshold amount and/or value to satisfy to determine ifthe recurring transactions have been designated for automatic execution.For example, the transaction analysis platform may determine thatrecurring transactions have not been designated for automatic executionif the dates of execution for each of the plurality of historicaltransactions do not satisfy a threshold date (for example, if the datesof execution for each of the plurality of historical transactions arenot one of the same 3 days each month (e.g., if the dates of executionfor each of the plurality of historical transactions do not occurbetween the first and the third of each month)). In someimplementations, the transaction analysis platform may determine thatthe recurring transactions have not been automatically executed based ona subset of the plurality of historical transactions corresponding tothe recurring transactions.

In some implementations, the transaction analysis platform may determinethat a historical transaction of the plurality of historicaltransactions associated with the recurring transactions was notdesignated for automatic execution. The transaction analysis platformmay determine that that the recurring transactions are not designated tobe automatically executed based on the determination that the singlehistorical transaction of the plurality of historical transactions wasnot designated for automatic execution. In some implementations, thesingle historical transaction of the plurality of historicaltransactions is a most recent transaction associated with the merchantaccount in the transaction log.

The transaction analysis platform may determine that recurringtransactions associated with a merchant account of the user are notdesignated for automatic execution based on a user input that indicateswhether the merchant account is associated with automatic execution. Insome implementations, the transaction analysis platform may determinethat recurring transactions associated with a merchant account of theuser are not designated for automatic execution based on informationreceived from the messaging platform and/or the merchant platform. Forexample, the transaction analysis platform may determine that recurringtransactions associated with a merchant account of the user are notdesignated for automatic execution based on a message from the merchantassociated with the merchant account that does not include a keyword(such as automatic, auto-pay, regular, predetermined, scheduled, and/orthe like) indicating that the merchant account is designated forautomatic execution.

In some implementations, the transaction analysis platform may determinethat recurring transactions associated with a merchant account of theuser are not designated for automatic execution based on metadataassociated with one or more of the plurality of historical transactionsassociated with the recurring transactions. In some implementations, thetransaction analysis platform analyzes the metadata of the most recenthistorical transaction of the plurality of historical transactions. Insome implementations, the transaction analysis platform analyzes themetadata of a subset of the plurality of historical transactions.

The transaction analysis platform may determine that, based ondetermining that the historical transaction is not designated forautomatic execution, that an execution of an upcoming transaction (e.g.,the next transaction due associated with the plurality of historicaltransactions) is not scheduled. For example, the transaction analysisplatform may determine that a plurality of historical transactionscorrespond to merchant ABC Co. and the transactions are made monthly(the transaction period). For this example, assume the date today isOct. 1, 2019. The transaction analysis platform may determine that thehistorical transaction corresponding to the month of September was notdesignated to be automatically executed. The transaction analysisplatform may determine that, based on the transaction pattern identifiedas described above, that an upcoming transaction is due in the month ofOctober. The transaction analysis platform may determine that, based ondetermining that the historical transaction corresponding to the monthof September was not designated to be automatically executed, theupcoming transaction corresponding to the month of October is notscheduled to be automatically executed.

As shown by reference number 160 a, the transaction analysis platformmay utilize a machine learning model to determine whether recurringtransactions are designated for automatic execution. The transactionanalysis platform may use one or more machine learning models such asone or more machine learning models described with regard to FIGS. 2 and3 or one or more machine learning models trained in a manner similar tothat described with regard to FIGS. 2 and 3 .

As further shown in FIG. 1C, and by reference number 170 a, thetransaction analysis platform may cause the transaction backend systemto execute an upcoming transaction associated with the recurringtransaction. The transaction analysis platform may determine atransaction amount associated with the upcoming transaction, atransaction period for the upcoming transaction (e.g., when thetransaction must be completed to avoid a missed transaction), a merchantassociated with the upcoming payment, a transaction account associatedwith the user and the upcoming payment, and/or the like.

The transaction analysis platform may determine that the upcomingtransaction includes a transfer of funds between a transaction accountassociated with the user and a merchant account associated with themerchant (e.g., a transaction account associated with the merchant). Thetransaction analysis platform may cause, based on the determination thatthe upcoming transaction is not scheduled to be automatically executedand before the expiration of the transaction period associated with theupcoming transaction passes, the upcoming transaction to beautomatically executed.

The transaction analysis platform may determine that a status (e.g.,amount of funds within the account, line of credit available to theaccount, a setting of the account, an expiration date of the account,and/or the like) of the transaction account of the user satisfies athreshold for performing the upcoming transaction. For example, thethreshold may relate to the transaction amount of the upcomingtransaction. The transaction analysis platform may, based on determiningthat the status of the transaction account of the user satisfies thethreshold for performing the upcoming transaction, cause the upcomingtransaction to be automatically executed using resources (e.g., funds,credit, and/or the like) associated with the transaction account of theuser.

The transaction analysis platform may, based on determining that thestatus of the transaction account of the user does not satisfy thethreshold, request a different transaction account associated with theuser to execute the transaction. The different transaction account maybe provided by the user when the user registers for and/or providespreauthorization to the transaction analysis platform. The transactionanalysis platform may, based on determining that the status of thetransaction account of the user does not satisfy the threshold, promptthe user to identify a different transaction account, urgently notifythe user via the user device, execute the transaction with the differenttransaction account, and/or the like.

The transaction analysis platform may cause the upcoming transaction tobe automatically executed by scheduling an execution of a transactioncorresponding to the upcoming transaction. The scheduling of theexecution of the transaction may be based on the transaction period ofthe upcoming transaction. For example, the transaction analysis platformmay schedule the execution of the transaction before the transactionperiod passes. In this way, the transaction analysis platform ensuresthat the upcoming transaction will not become a missed transaction.

The transaction analysis platform may cause the upcoming transaction tobe automatically executed by designating a transaction corresponding tothe upcoming transaction for automatic execution. The transactionanalysis platform may designate the transaction corresponding to theupcoming transaction for automatic execution by communicating with theuser device and/or the merchant platform. In some implementations, thetransaction analysis platform may designate the transactioncorresponding to the upcoming transaction for automatic execution viaautomated web-based interactions. In some implementations, thetransaction analysis platform may designate the transactioncorresponding to the upcoming transaction for automatic execution byprompting the user to perform an interaction using the user device whichcauses the transaction associated with the upcoming transaction to bedesignated for automatic execution. In some implementations, thetransaction analysis platform requests confirmation from the user priorto designating the transaction corresponding to the upcoming transactionfor automatic execution. The request for confirmation may include arequest to designate a transaction account to be associated with thetransaction corresponding to the upcoming transaction.

The transaction analysis platform may cause the upcoming transaction tobe automatically executed by executing, via the transaction back endsystem, a transaction with the merchant account that corresponds to theupcoming transaction. In some implementations, executing the transactionwith the merchant account that corresponds to the upcoming transactionincludes transferring resources from a transaction account associatedwith the user to a transaction account associated with the merchant. Theexecution of the transaction corresponding to the upcoming transactionmay be based on a characteristic (such as transaction amount, date ofexecution, merchant transaction account information, and/or the like) ofthe plurality of historical transactions corresponding to the upcomingtransaction.

The transaction analysis platform may create a transaction entry thatcorresponds to the transaction corresponding to the upcoming transactionin the transaction log. For example, after, or at the same time, asexecuting the transaction corresponding to the upcoming transaction, thetransaction analysis platform may create a new entry, or cause a newentry to be created, in the transaction log of the transaction accountassociated with the user.

The transaction analysis platform may send a notification to the userdevice of the user to indicate that the transaction corresponding to theupcoming transaction has been executed. The notification may includetransaction information (such as transaction amount, date of execution,merchant associated with the transaction, and/or the like).

The transaction analysis platform may, before causing the transactioncorresponding to the upcoming transaction to be executed, request theuser to authorize the transaction. The transaction analysis platform mayexecute the transaction based on the user providing authorization toperform the transaction.

The transaction analysis platform may cause the transactioncorresponding to the upcoming transaction to be executed in conjunctionwith the transaction backend system. The transaction analysis platformmay communicate with the transaction backend system. The transactionbackend system may communicate with the merchant account platform toexecute the transaction. The merchant account platform may containtransaction account information related to the transaction account ofthe merchant. The transaction analysis platform may transmit a requestto the transaction backend system to execute a transaction. Thetransaction backend system may be associated with a service provider.The service provider may be a financial institution, a mobile paymentcompany, and/or the like.

The transaction backend system may store transaction account information(such as transaction account identifier information, user information,device information, and/or the like) related to the service provider.The transaction account information may be associated with an accountcorresponding to the user and/or the merchant. For example, when a userregisters for a transaction account with the service provider, thetransaction account information may be provided by or to the user andassociated with the transaction account of the user. The transactionaccount information may be stored by the transaction backend system.

The transaction backend system may receive the request from thetransaction analysis platform to execute the transaction. The requestfrom the transaction analysis platform may include user information(such as user device information, user transaction account identifierinformation, user login information, and/or the like). The request fromthe transaction analysis platform may include merchant information (suchas merchant platform information, merchant transaction accountidentifier information, and/or the like). In some implementations, thecommunication between the transaction analysis platform and thetransaction backend system is secure (e.g., encrypted).

The transaction backend system may execute the transaction requested bythe transaction analysis platform in response to receiving the requesttransmitted by the transaction analysis platform. The transactionbackend system may communicate with the transaction analysis platformand/or the user device if the transaction backend system is unable toexecute the transaction. For example, the transaction backend system mayrequire additional information to execute the transaction. Thetransaction backend system may transmit a request to transactionanalysis platform for the additional information required. For example,the transaction backend system may search for a transaction accountassociated with the user and/or the merchant. If the transaction backendsystem is unable to locate a transaction account associated with theuser and/or the merchant, the transaction backend system may transmit amessage to the transaction analysis platform that no transaction accountwas found that was associated with the user and/or the merchant.

The transaction backend system may alert or notify the user if thetransaction backend system is unable to execute the transaction. Thetransaction backend system may alert the user by sending a message tothe user device indicating that the transaction backend system is unableto execute the transaction. The message to the user device may indicateone or more actions the user may perform (e.g., via the user deviceand/or the like) to complete the transaction (e.g., steps to completethe transaction, a platform to access to complete the transaction,information required to complete the transaction, and/or the like).

As further shown in FIG. 1C, and by reference number 150 b, thetransaction analysis platform may determine that the recurringtransaction is designated to be automatically executed. The transactionanalysis platform may determine that the recurring transaction isdesignated to be automatically executed in a manner similar to that asdescribed above with respect to FIGS. 1A-1C, such as with respect toreference number 150 a. The transaction analysis platform may determinethat the recurring transaction is designated to be automaticallyexecuted based on a user input received by the transaction analysissystem.

As shown by reference number 160 b, the transaction analysis platformmay utilize a machine learning model to determine whether recurringtransactions are designated for automatic execution. The transactionanalysis platform may use one or more machine learning models such asone or more machine learning models described with regard to FIGS. 2 and3 or one or more machine learning models trained in a manner similar tothat described with regard to FIGS. 2 and 3 .

As shown by reference number 170 b, the transaction analysis platformmay verify that an upcoming transaction associated with the recurringtransactions designated for automatic execution is likely to beprocessed and/or is capable of being processed. For example, thetransaction analysis platform may determine that the status of thetransaction account of the user satisfies a threshold for performing theupcoming transaction. For example, the threshold may relate to thetransaction amount of the upcoming transaction. The transaction analysisplatform may, based on determining that the status of the transactionaccount of the user satisfies the threshold for performing the upcomingtransaction, verify that the upcoming transaction is likely to beautomatically executed using resources (e.g., funds, credit, and/or thelike) associated with the transaction account of the user.

The transaction analysis platform may communicate with the merchantaccount platform to verify that the upcoming transaction associated withthe recurring transactions designated for automatic execution is likelyto be processed and/or is capable of being processed. The transactionanalysis platform may confirm that the merchant account platformincludes all the required information (such as transaction accountinformation, transaction information, and/or the like) to execute theupcoming transaction. In some implementations, the transactions analysisplatform may confirm information that may prevent a transaction fromoccurring (such as expiration date, amount of resources available,and/or the like) related to the transaction account associated with theuser stored in the merchant account platform.

The transaction analysis platform may, based on not verifying that theupcoming transaction is likely to be automatically executed, urgentlynotify the user via the transaction analysis platform and/or the userdevice. Additionally, or alternatively, the transaction analysisplatform may use a different transaction account associated with theuser to execute the upcoming transaction associated with the recurringtransactions designated for automatic execution. The transactionanalysis platform may determine transaction account information relatedto the different transaction account in a manner similar to that asdescribed above.

In this way, the transaction analysis platform conserves computingresources of the user device and/or network resources that would havebeen otherwise used to locate a merchant platform (e.g., an application,a website, and/or the like) associated with the execution of theupcoming transaction, determine the transaction period, determine thetransaction amount, provide information associated with the transactionaccount associated with the user to the merchant platform, and/orrequest or initiate a transaction from the transaction account tocomplete the upcoming transaction. Additionally, some implementationsdescribed herein may enable the transaction analysis platform to executethe upcoming transaction before the user misses the upcoming transaction(e.g., does not execute the upcoming transaction and/or does notexecuted the upcoming transaction within the transaction period). As aresult, the transaction analysis platform conserves computing resourcesof the user device and/or network resources that would have otherwisebeen used to identify the missed transaction, investigate the missedtransaction, execute the missed transaction, and/or contact the merchantassociated with the missed transaction. The merchant associated with themissed transaction may conserve computing resources and/or networkresources that would have otherwise been used to identify, detect,investigate, execute, and/or remedy the missed transaction.

As indicated above, FIGS. 1A-1C are provided as one or more examples.Other examples may differ from what is described with regard to FIGS.1A-1C. The number and arrangement of devices and/or platforms shown inFIGS. 1A-1C are provided as one or more examples. In practice, there maybe additional devices and/or platforms, fewer devices and/or platforms,different devices and/or platforms, or differently arranged devicesand/or platforms than those shown in FIGS. 1A-1C. Furthermore, two ormore devices and/or platforms shown in FIGS. 1A-1C may be implementedwithin a single device and/or platform, or a single device and/orplatform shown in FIGS. 1A-1C may be implemented as multiple,distributed devices and/or platforms. Additionally, or alternatively, aset of devices and/or platforms (e.g., one or more device and/orplatform) of FIGS. 1A-1C may perform one or more functions described asbeing performed by another set of devices and/or platforms of FIGS.1A-1C.

FIG. 2 is a diagram illustrating an example 200 of training a machinelearning model. The machine learning model training described herein maybe performed using a machine learning system. The machine learningsystem may include a computing device, a server, a cloud computingenvironment, and/or the like, such as a transaction analysis platform, auser device, a server device, and/or a transaction backend system.

As shown by reference number 205, a machine learning model may betrained using a set of observations. The set of observations may beobtained and/or input from historical data, such as data gathered duringone or more processes described herein. For example, the set ofobservations may include data gathered from user interaction with and/oruser input to determine if a transaction is designated for automaticexecution, as described elsewhere herein. In some implementations, themachine learning system may receive the set of observations (e.g., asinput) from the transaction analysis platform, the user device, theserver device, and/or the transaction backend system.

As shown by reference number 210, a feature set may be derived from theset of observations. The feature set may include a set of variabletypes. A variable type may be referred to as a feature. A specificobservation may include a set of variable values corresponding to theset of variable types. A set of variables values may be specific to anobservation. In some cases, different observations may be associatedwith different sets of variable values, sometimes referred to as featurevalues. In some implementations, the machine learning system maydetermine variable values for a specific observation based on inputreceived from the transaction analysis platform, the user device, theserver device, and/or the transaction backend system. For example, themachine learning system may identify a feature set (e.g., one or morefeatures and/or corresponding feature values) from structured data inputto the machine learning system, such as by extracting data from aparticular column of a table, extracting data from a particular field ofa form, extracting data from a particular field of a message, extractingdata received in a structured data format, and/or the like. In someimplementations, the machine learning system may determine features(e.g., variables types) for a feature set based on input received fromthe transaction analysis platform, the user device, the server device,and/or the transaction backend system, such as by extracting orgenerating a name for a column, extracting or generating a name for afield of a form and/or a message, extracting or generating a name basedon a structured data format, and/or the like. Additionally, oralternatively, the machine learning system may receive input from anoperator to determine features and/or feature values. In someimplementations, the machine learning system may perform naturallanguage processing and/or another feature identification technique toextract features (e.g., variable types) and/or feature values (e.g.,variable values) from text (e.g., unstructured data) input to themachine learning system, such as by identifying keywords and/or valuesassociated with those keywords from the text.

As an example, a feature set for a set of observations may include afirst feature of an average transaction execution date difference (e.g.,the average difference between the date of execution (for example, theday of the month) of a plurality of historical transactionscorresponding to the transaction), a second feature of an averagetransaction amount difference (e.g., the average difference between eachtransaction amount of each of the plurality of historical transactionscorresponding to the transaction), a third feature of whether the periodof execution between historical transactions is similar (thedetermination of whether the period of execution between each historicaltransaction of the plurality of historical transactions is similar maybe based on whether the average period of execution between historicaltransactions satisfies a threshold), and so on. As shown, for a firstobservation, the first feature may have a value of “0 days,” the secondfeature may have a value of “$0” (e.g., each of the plurality ofhistorical transactions had the same transaction amount), the thirdfeature may have a value of “Yes,” and so on. These features and featurevalues are provided as examples, and may differ in other examples. Forexample, the feature set may include one or more of the followingfeatures: whether an input from a user has been received indicating thetransaction is designated for automatic execution; whether a transactionpattern has been identified indicating one or more historicaltransactions corresponding to the transaction was designated forautomatic execution; whether one or more keywords indicating thetransaction is designated for automatic execution has been identified;whether the type of good and/or service associated with the transactionis one which has been identified as being associated with automaticexecution (such as a mortgage transaction, a lease transaction, autility transaction, and/or the like); whether the date of execution ofhistorical transactions corresponding to the transaction are related;and/or the like. In some implementations, the machine learning systemmay pre-process and/or perform dimensionality reduction to reduce thefeature set and/or combine features of the feature set to a minimumfeature set. A machine learning model may be trained on the minimumfeature set, thereby conserving resources of the machine learning system(e.g., processing resources, memory, and/or the like) used to train themachine learning model.

As shown by reference number 215, the set of observations may beassociated with a target variable type. The target variable type mayrepresent a variable having a numeric value (e.g., an integer value, afloating point value, and/or the like), may represent a variable havinga numeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiples classes, classifications,labels, and/or the like), may represent a variable having a Booleanvalue (e.g., 0 or 1, True or False, Yes or No), and/or the like. Atarget variable type may be associated with a target variable value, anda target variable value may be specific to an observation. In somecases, different observations may be associated with different targetvariable values. As shown in FIG. 2 , the target variable may correspondto whether a transaction is automatic (e.g., designated for automaticexecution).

The target variable may represent a value that a machine learning modelis being trained to predict, and the feature set may represent thevariables that are input to a trained machine learning model to predicta value for the target variable. The set of observations may includetarget variable values so that the machine learning model can be trainedto recognize patterns in the feature set that lead to a target variablevalue. A machine learning model that is trained to predict a targetvariable value may be referred to as a supervised learning model, apredictive model, and/or the like. When the target variable type isassociated with continuous target variable values (e.g., a range ofnumbers and/or the like), the machine learning model may employ aregression technique. When the target variable type is associated withcategorical target variable values (e.g., classes, labels, and/or thelike), the machine learning model may employ a classification technique.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable (or thatinclude a target variable, but the machine learning model is not beingexecuted to predict the target variable). This may be referred to as anunsupervised learning model, an automated data analysis model, anautomated signal extraction model, and/or the like. In this case, themachine learning model may learn patterns from the set of observationswithout labeling or supervision, and may provide output that indicatessuch patterns, such as by using clustering and/or association toidentify related groups of items within the set of observations.

As further shown, the machine learning system may partition the set ofobservations into a training set 220 that includes a first subset ofobservations, of the set of observations, and a test set 225 thatincludes a second subset of observations of the set of observations. Thetraining set 220 may be used to train (e.g., fit, tune, and/or the like)the machine learning model, while the test set 225 may be used toevaluate a machine learning model that is trained using the training set220. For example, for supervised learning, the training set 220 may beused for initial model training using the first subset of observations,and the test set 225 may be used to test whether the trained modelaccurately predicts target variables in the second subset ofobservations. In some implementations, the machine learning system maypartition the set of observations into the training set 220 and the testset 225 by including a first portion or a first percentage of the set ofobservations in the training set 220 (e.g., 75%, 80%, or 85%, amongother examples) and including a second portion or a second percentage ofthe set of observations in the test set 225 (e.g., 25%, 20%, or 15%,among other examples). In some implementations, the machine learningsystem may randomly select observations to be included in the trainingset 220 and/or the test set 225.

As shown by reference number 230, the machine learning system may traina machine learning model using the training set 220. This training mayinclude executing, by the machine learning system, a machine learningalgorithm to determine a set of model parameters based on the trainingset 220. In some implementations, the machine learning algorithm mayinclude a regression algorithm (e.g., linear regression, logisticregression, and/or the like), which may include a regularized regressionalgorithm (e.g., Lasso regression, Ridge regression, Elastic-Netregression, and/or the like). Additionally, or alternatively, themachine learning algorithm may include a decision tree algorithm, whichmay include a tree ensemble algorithm (e.g., generated using baggingand/or boosting), a random forest algorithm, a boosted trees algorithm,and/or the like. A model parameter may include an attribute of a machinelearning model that is learned from data input into the model (e.g., thetraining set 220). For example, for a regression algorithm, a modelparameter may include a regression coefficient (e.g., a weight). For adecision tree algorithm, a model parameter may include a decision treesplit location, as an example.

As shown by reference number 235, the machine learning system may useone or more hyperparameter sets 240 to tune the machine learning model.A hyperparameter may include a structural parameter that controlsexecution of a machine learning algorithm by the machine learningsystem, such as a constraint applied to the machine learning algorithm.Unlike a model parameter, a hyperparameter is not learned from datainput into the model. An example hyperparameter for a regularizedregression algorithm includes a strength (e.g., a weight) of a penaltyapplied to a regression coefficient to mitigate overfitting of themachine learning model to the training set 220. The penalty may beapplied based on a size of a coefficient value (e.g., for Lassoregression, such as to penalize large coefficient values), may beapplied based on a squared size of a coefficient value (e.g., for Ridgeregression, such as to penalize large squared coefficient values), maybe applied based on a ratio of the size and the squared size (e.g., forElastic-Net regression), may be applied by setting one or more featurevalues to zero (e.g., for automatic feature selection), and/or the like.Example hyperparameters for a decision tree algorithm include a treeensemble technique to be applied (e.g., bagging, boosting, a randomforest algorithm, a boosted trees algorithm, and/or the like), a numberof features to evaluate, a number of observations to use, a maximumdepth of each decision tree (e.g., a number of branches permitted forthe decision tree), a number of decision trees to include in a randomforest algorithm, and/or the like.

To train a machine learning model, the machine learning system mayidentify a set of machine learning algorithms to be trained (e.g., basedon operator input that identifies the one or more machine learningalgorithms, based on random selection of a set of machine learningalgorithms, and/or the like), and may train the set of machine learningalgorithms (e.g., independently for each machine learning algorithm inthe set) using the training set 220. The machine learning system maytune each machine learning algorithm using one or more hyperparametersets 240 (e.g., based on operator input that identifies hyperparametersets 240 to be used, based on randomly generating hyperparameter values,and/or the like). The machine learning system may train a particularmachine learning model using a specific machine learning algorithm and acorresponding hyperparameter set 240. In some implementations, themachine learning system may train multiple machine learning models togenerate a set of model parameters for each machine learning model,where each machine learning model corresponds to a different combinationof a machine learning algorithm and a hyperparameter set 240 for thatmachine learning algorithm.

In some implementations, the machine learning system may performcross-validation when training a machine learning model. Crossvalidation can be used to obtain a reliable estimate of machine learningmodel performance using only the training set 220, and without using thetest set 225, such as by splitting the training set 220 into a number ofgroups (e.g., based on operator input that identifies the number ofgroups, based on randomly selecting a number of groups, and/or the like)and using those groups to estimate model performance. For example, usingk-fold cross-validation, observations in the training set 220 may besplit into k groups (e.g., in order or at random). For a trainingprocedure, one group may be marked as a hold-out group, and theremaining groups may be marked as training groups. For the trainingprocedure, the machine learning system may train a machine learningmodel on the training groups and then test the machine learning model onthe hold-out group to generate a cross-validation score. The machinelearning system may repeat this training procedure using differenthold-out groups and different test groups to generate a cross-validationscore for each training procedure. In some implementations, the machinelearning system may independently train the machine learning model ktimes, with each individual group being used as a hold-out group onceand being used as a training group k−1 times. The machine learningsystem may combine the cross-validation scores for each trainingprocedure to generate an overall cross-validation score for the machinelearning model. The overall cross-validation score may include, forexample, an average cross-validation score (e.g., across all trainingprocedures), a standard deviation across cross-validation scores, astandard error across cross-validation scores, and/or the like.

In some implementations, the machine learning system may performcross-validation when training a machine learning model by splitting thetraining set into a number of groups (e.g., based on operator input thatidentifies the number of groups, based on randomly selecting a number ofgroups, and/or the like). The machine learning system may performmultiple training procedures and may generate a cross-validation scorefor each training procedure. The machine learning system may generate anoverall cross-validation score for each hyperparameter set 240associated with a particular machine learning algorithm. The machinelearning system may compare the overall cross-validation scores fordifferent hyperparameter sets 240 associated with the particular machinelearning algorithm, and may select the hyperparameter set 240 with thebest (e.g., highest accuracy, lowest error, closest to a desiredthreshold, and/or the like) overall cross-validation score for trainingthe machine learning model. The machine learning system may then trainthe machine learning model using the selected hyperparameter set 240,without cross-validation (e.g., using all of data in the training set220 without any hold-out groups), to generate a single machine learningmodel for a particular machine learning algorithm. The machine learningsystem may then test this machine learning model using the test set 225to generate a performance score, such as a mean squared error (e.g., forregression), a mean absolute error (e.g., for regression), an area underreceiver operating characteristic curve (e.g., for classification),and/or the like. If the machine learning model performs adequately(e.g., with a performance score that satisfies a threshold), then themachine learning system may store that machine learning model as atrained machine learning model 245 to be used to analyze newobservations, as described below in connection with FIG. 3 .

In some implementations, the machine learning system may performcross-validation, as described above, for multiple machine learningalgorithms (e.g., independently), such as a regularized regressionalgorithm, different types of regularized regression algorithms, adecision tree algorithm, different types of decision tree algorithms,and/or the like. Based on performing cross-validation for multiplemachine learning algorithms, the machine learning system may generatemultiple machine learning models, where each machine learning model hasthe best overall cross-validation score for a corresponding machinelearning algorithm. The machine learning system may then train eachmachine learning model using the entire training set 220 (e.g., withoutcross-validation), and may test each machine learning model using thetest set 225 to generate a corresponding performance score for eachmachine learning model. The machine learning model may compare theperformance scores for each machine learning model, and may select themachine learning model with the best (e.g., highest accuracy, lowesterror, closest to a desired threshold, and/or the like) performancescore as the trained machine learning model 245.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2 . For example,the machine learning model may be trained using a different process thanwhat is described in connection with FIG. 2 . Additionally, oralternatively, the machine learning model may employ a different machinelearning algorithm than what is described in connection with FIG. 2 ,such as a Bayesian estimation algorithm, a k-nearest neighbor algorithm,an a priori algorithm, a k-means algorithm, a support vector machinealgorithm, a neural network algorithm (e.g., a convolutional neuralnetwork algorithm), a deep learning algorithm, and/or the like.

FIG. 3 is a diagram illustrating an example 300 of applying a trainedmachine learning model to a new observation. The new observation may beinput to a machine learning system that stores a trained machinelearning model 305. In some implementations, the trained machinelearning model 305 may be the trained machine learning model 245described above in connection with FIG. 2 . The machine learning systemmay include a computing device, a server, a cloud computing environment,and/or the like, such as a transaction analysis platform, a user device,a server device, and/or a transaction backend system.

As shown by reference number 310, the machine learning system mayreceive a new observation (or a set of new observations), and may inputthe new observation to the machine learning model 305. As shown, the newobservation may include a first feature of an average transactionexecution date difference (e.g., the average difference between the dateof execution (for example, the day of the month) of a plurality ofhistorical transactions corresponding to the transaction), a secondfeature of an average transaction amount difference (e.g., the averagedifference of the value of each of historical transaction of theplurality of historical transactions corresponding to the transaction),a third feature of whether the period of execution between historicaltransactions is similar (the determination of whether the period ofexecution between each historical transaction of the plurality ofhistorical transactions is similar may be based on whether the averageperiod of execution between historical transactions satisfies athreshold), and so on, as an example. The machine learning system mayapply the trained machine learning model 305 to the new observation togenerate an output (e.g., a result). The type of output may depend onthe type of machine learning model and/or the type of machine learningtask being performed. For example, the output may include a predicted(e.g., estimated) value of target variable (e.g., a value within acontinuous range of values, a discrete value, a label, a class, aclassification, and/or the like), such as when supervised learning isemployed. Additionally, or alternatively, the output may includeinformation that identifies a cluster to which the new observationbelongs, information that indicates a degree of similarity between thenew observations and one or more prior observations (e.g., which mayhave previously been new observations input to the machine learningmodel and/or observations used to train the machine learning model),and/or the like, such as when unsupervised learning is employed.

In some implementations, the trained machine learning model 305 maypredict a value of “No” for the target variable of whether thetransaction is automatic (e.g., designated for automatic execution) forthe new observation, as shown by reference number 315. Based on thisprediction (e.g., based on the value having a particularlabel/classification, based on the value satisfying or failing tosatisfy a threshold, and/or the like), the machine learning system mayprovide a recommendation, such as to execute the transaction, to executethe transaction before a transaction period associated with thetransaction passes, to schedule the transaction to be executed, and/orthe like. Additionally, or alternatively, the machine learning systemmay perform an automated action and/or may cause an automated action tobe performed (e.g., by instructing another device to perform theautomated action), such as automatically executing the transaction,automatically executing the transaction before the transaction periodassociated with the transaction passes, scheduling the transaction to beexecuted, and/or the like. As another example, if the machine learningsystem were to predict a value of “Yes” for the target variable ofwhether the transaction is automatic (e.g., designated for automaticexecution), then the machine learning system may provide a differentrecommendation (e.g., to provide a reminder related to the transaction,to no longer track and/or process future transactions associated withthe transaction, and/or the like) and/or may perform or causeperformance of a different automated action (e.g., to verify that atransaction account associated with the transaction is capable ofexecuting the transaction, and/or the like). In some implementations,the recommendation and/or the automated action may be based on thetarget variable value having a particular label (e.g., classification,categorization, and/or the like), may be based on whether the targetvariable value satisfies one or more threshold (e.g., whether the targetvariable value is greater than a threshold, is less than a threshold, isequal to a threshold, falls within a range of threshold values, and/orthe like), and/or the like.

In some implementations, the trained machine learning model 305 mayclassify (e.g. cluster) the new observation in a particular cluster, asshown by reference number 320. The observations within a cluster mayhave a threshold degree of similarity. Based on classifying the newobservation in the particular cluster, the machine learning system mayprovide a recommendation, such as to execute the transaction, to executethe transaction before a transaction period associated with thetransaction passes, to schedule the transaction to be executed, and/orthe like. Additionally, or alternatively, the machine learning systemmay perform an automated action and/or may cause an automated action tobe performed (e.g., by instructing another device to perform theautomated action), such as automatically executing the transaction,automatically executing the transaction before the transaction periodassociated with the transaction passes, scheduling the transaction to beexecuted, and/or the like. As another example, if the machine learningsystem were to classify the new observation in a different cluster, thenthe machine learning system may provide a different recommendation(e.g., to provide a reminder related to the transaction, to no longertrack and/or process future transactions associated with thetransaction, and/or the like) and/or may perform or cause performance ofa different automated action (e.g., to verify that a transaction accountassociated with the transaction is capable of executing the transaction,and/or the like).

In this way, the machine learning system may apply a rigorous andautomated process to determine whether a transaction is automatic (e.g.,designated for automatic execution). The machine learning system enablesrecognition and/or identification of tens, hundreds, thousands, ormillions of features and/or feature values for tens, hundreds,thousands, or millions of observations, thereby increasing an accuracyand consistency of determining whether a transaction is automatic (e.g.,designated for automatic execution) relative to requiring computingresources to be allocated for tens, hundreds, or thousands of operatorsto manually determine whether a transaction is automatic (e.g.,designated for automatic execution) using the features or featurevalues.

As indicated above, FIG. 3 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 3 .

FIG. 4 is a diagram of an example environment 400 in which systemsand/or methods, described herein, may be implemented. As shown in FIG. 4, environment 400 may include a transaction analysis platform 410, acloud computing environment 420, one or more user devices 430 (referredto herein individually as user device 430 or collectively as userdevices 430), one or more server devices 440 (referred to hereinindividually as server device 440 or collectively as server device 440),a transaction backend platform 450, and a network 460. In someimplementations, the cloud computing environment 420 may host thetransaction analysis platform 410 using one or more computing resources415. Devices of environment 400 may interconnect via wired connections,wireless connections, or a combination of wired and wirelessconnections.

Transaction analysis platform 410 may include one or more computingresources to automatically execute a transaction based on transactionlog analysis. For example, transaction analysis platform 410 may be aplatform implemented by cloud computing environment 420 that may receivea preauthorization associated with missed transaction prevention for atransaction account of a user, monitor a transaction log of thetransaction account, identify a transaction pattern associated with amerchant account based on a plurality of historical transactionsidentified in the transaction log being associated with the merchantaccount, determine that a historical transaction of the plurality ofhistorical transactions was not automatically executed, determine that,based on the historical transaction not being automatically executed,that an upcoming transaction associated with the plurality of historicaltransactions is not scheduled to be executed, and/or cause the upcomingtransaction to be automatically executed before a transaction periodassociated with the merchant account passes. In some implementations,transaction analysis platform 410 is implemented by computing resources415 of cloud computing environment 420.

Transaction analysis platform 410 may include a server device or a groupof server devices. In some implementations, transaction analysisplatform 410 may be hosted in cloud computing environment 420. Notably,while implementations described herein may describe transaction analysisplatform 410 as being hosted in cloud computing environment 420, in someimplementations, transaction analysis platform 410 may benon-cloud-based or may be partially cloud-based.

Cloud computing environment 420 includes an environment that deliverscomputing as a service, whereby shared resources, services, and/or thelike may be provided to the transaction analysis platform 410, the userdevices 430, the server devices 440, the transaction backend platform450, and/or the like. Cloud computing environment 420 may providecomputation, software, data access, storage, and/or other services thatdo not require end-user knowledge of a physical location andconfiguration of a system and/or a device that delivers the services. Asshown, cloud computing environment 420 may include transaction analysisplatform 410 and computing resource 415.

Computing resource 415 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource415 may host transaction analysis platform 410. The cloud resources mayinclude compute instances executing in computing resource 415, storagedevices provided in computing resource 415, data transfer devicesprovided by computing resource 415, and/or the like. In someimplementations, computing resource 415 may communicate with othercomputing resources 415 via wired connections, wireless connections, ora combination of wired and wireless connections.

As further shown in FIG. 4 , computing resource 415 may include a groupof cloud resources, such as one or more applications (“APPs”) 415-1, oneor more virtual machines (“VMs”) 415-2, virtualized storage (“VSs”)415-3, one or more hypervisors (“HYPs”) 415-4, or the like.

Application 415-1 includes one or more software applications that may beprovided to or accessed by user device 430. Application 415-1 mayeliminate a need to install and execute the software applications onuser device 430. For example, application 415-1 may include softwareassociated with transaction analysis platform 410 and/or any othersoftware capable of being provided via cloud computing environment 420.In some implementations, one application 415-1 may send/receiveinformation to/from one or more other applications 415-1, via virtualmachine 415-2.

Virtual machine 415-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 415-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 415-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 415-2 may execute on behalf of a user(e.g., user device 430), and may manage infrastructure of cloudcomputing environment 420, such as data management, synchronization, orlong-duration data transfers.

Virtualized storage 415-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 415. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 415-4 provides hardware virtualization techniques that allowmultiple operating systems (e.g., “guest operating systems”) to executeconcurrently on a host computer, such as computing resource 415.Hypervisor 415-4 may present a virtual operating platform to the “guestoperating systems” and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

User device 430 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith a user (e.g., recurring transaction related information). Forexample, user device 430 may include a communication and/or computingdevice, such as a mobile phone (e.g., a smart phone, a radiotelephone,etc.), a laptop computer, a tablet computer, a handheld computer, adesktop computer, a gaming device, a wearable communication device(e.g., a smart wristwatch, a pair of smart eyeglasses, etc.), or asimilar type of device.

Server device 440 includes one or more devices capable of storing,processing, and/or routing information associated with recurringtransaction related activity. For example, one or more of server devices440 may host one or more transaction account platforms, merchantplatforms, messaging platforms, merchant account platforms, and/or thelike (e.g., similar to the platforms in FIG. 1A-1C). In someimplementations, server device 440 may include a communication interfacethat allows server device 440 to receive information from and/ortransmit information to other devices in environment 400.

Transaction backend platform 450 includes one or more devices capable ofreceiving, generating, storing, processing, and providing informationassociated with managing a transaction account of a user. For example,transaction account platform 450 may be associated with one or moreserver devices that include a communication interface that allowstransaction backend platform 450 to receive information from and/ortransmit information to other devices in environment 400. In someimplementations, transaction backend platform 450 may include and/orhave access to a data structure used to maintain a transaction log of anaccount of the user, profile information associated with the user,preferences associated with the user, and/or the like. In someimplementations, the transaction backend platform 450 may be similar tothe transaction account platform discussed with respect to FIGS. 1A-1C.

Network 460 includes one or more wired and/or wireless networks. Forexample, network 460 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, and/or the like), a public land mobile network(PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the PublicSwitched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, and/or the like, and/or a combination of these orother types of networks.

The number and arrangement of devices, platforms, and networks shown inFIG. 4 are provided as one or more examples. In practice, there may beadditional devices, platforms, and/or networks, fewer devices,platforms, and/or networks, different devices, platforms, and/ornetworks, or differently arranged devices, platforms, and/or networksthan those shown in FIG. 4 . Furthermore, two or more devices and/orplatforms shown in FIG. 4 may be implemented within a single deviceand/or platform, or a single device and/or platform shown in FIG. 4 maybe implemented as multiple, distributed devices and/or platforms.Additionally, or alternatively, a set of devices and/or platforms (e.g.,one or more devices and/or platforms) of environment 400 may perform oneor more functions described as being performed by another set of devicesand/or platforms of environment 400.

FIG. 5 is a diagram of example components of a device 500. Device 500may correspond to transaction analysis platform 410, one or more userdevices 430, one or more server devices 440, and/or transaction backendplatform 450. In some implementations, transaction analysis platform410, one or more user devices 430, one or more server devices 440,and/or transaction backend platform 450 may include one or more devices500 and/or one or more components of device 500. As shown in FIG. 5 ,device 500 may include a bus 510, a processor 520, a memory 530, astorage component 540, an input component 550, an output component 560,and a communication interface 570.

Bus 510 includes a component that permits communication among multiplecomponents of device 500. Processor 520 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 520is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 520includes one or more processors capable of being programmed to perform afunction. Memory 530 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 520.

Storage component 540 stores information and/or software related to theoperation and use of device 500. For example, storage component 540 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 550 includes a component that permits device 500 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 550 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 560 includes a component thatprovides output information from device 500 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 570 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 500 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 570 may permit device500 to receive information from another device and/or provideinformation to another device. For example, communication interface 570may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 500 may perform one or more processes described herein. Device500 may perform these processes based on processor 520 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 530, and/or storage component 540. As usedherein, the term “computer-readable medium” refers to a non-transitorymemory device. A memory device includes memory space within a singlephysical storage device or memory space spread across multiple physicalstorage devices.

Software instructions may be read into memory 530 and/or storagecomponent 540 from another computer-readable medium or from anotherdevice via communication interface 570. When executed, softwareinstructions stored in memory 530 and/or storage component 540 may causeprocessor 520 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 5 are provided asan example. In practice, device 500 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 5 . Additionally, or alternatively,a set of components (e.g., one or more components) of device 500 mayperform one or more functions described as being performed by anotherset of components of device 500.

FIG. 6 is a flow chart of an example process 600 for automatictransaction execution based on transaction log analysis. In someimplementations, one or more process blocks of FIG. 6 may be performedby a device (e.g., user device 430 and/or transaction analysis platform410). In some implementations, one or more process blocks of FIG. 6 maybe performed by another device or a group of devices separate from orincluding the device, such as a server device (e.g., server device 440),a transaction backend platform (e.g., transaction backend platform 450),and/or the like.

As shown in FIG. 6 , process 600 may include receiving apreauthorization associated with missed transaction prevention for atransaction account of a user, wherein the missed transaction preventioninvolves preventing an occurrence of a missed transaction associatedwith merchant accounts of the user (block 610). For example, the device(e.g., using processor 520, memory 530, storage component 540, inputcomponent 550, output component 560, communication interface 570, and/orthe like) may receive a preauthorization associated with missedtransaction prevention for a transaction account of a user, as describedabove. In some implementations, the missed transaction preventioninvolves preventing an occurrence of a missed transaction associatedwith merchant accounts of the user.

As further shown in FIG. 6 , process 600 may include monitoring, basedon the preauthorization, a transaction log of the transaction account(block 620). For example, the device (e.g., using processor 520, memory530, storage component 540, input component 550, output component 560,communication interface 570, and/or the like) may monitor, based on thepreauthorization, a transaction log of the transaction account, asdescribed above.

As further shown in FIG. 6 , process 600 may include identifying atransaction pattern associated with a merchant account, wherein thetransaction pattern is identified based on a plurality of historicaltransactions identified in the transaction log being associated with themerchant account (block 630). For example, the device (e.g., usingprocessor 520, memory 530, storage component 540, input component 550,output component 560, communication interface 570, and/or the like) mayidentify a transaction pattern associated with a merchant account, asdescribed above. In some implementations, the transaction pattern isidentified based on a plurality of historical transactions identified inthe transaction log being associated with the merchant account.

As further shown in FIG. 6 , process 600 may include determining, basedon the transaction pattern, that a historical transaction of theplurality of historical transactions is not designated for automaticexecution (block 640). For example, the device (e.g., using processor520, memory 530, storage component 540, input component 550, outputcomponent 560, communication interface 570, and/or the like) maydetermine, based on the transaction pattern, that a historicaltransaction of the plurality of historical transactions is notdesignated for automatic execution, as described above.

As further shown in FIG. 6 , process 600 may include determining, basedon determining that the historical transaction is not designated forautomatic execution, that an execution of an upcoming transactioncorresponding to the plurality of historical transactions is notscheduled (block 650). For example, the device (e.g., using processor520, memory 530, storage component 540, input component 550, outputcomponent 560, communication interface 570, and/or the like) maydetermine, based on determining that the historical transaction is notdesignated for automatic execution, that an execution of an upcomingtransaction corresponding to the plurality of historical transactions isnot scheduled, as described above.

As further shown in FIG. 6 , process 600 may include causing an accounttransaction associated with the upcoming transaction to be automaticallyexecuted before a transaction period expiration, that is associated withthe merchant account, passes (block 660). For example, the device (e.g.,using processor 520, memory 530, storage component 540, input component550, output component 560, communication interface 570, and/or the like)may cause an account transaction associated with the upcomingtransaction to be automatically executed before a transaction periodexpiration, that is associated with the merchant account, passes, asdescribed above.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the preauthorization is received inassociation with a verification process that authenticates that the userprovided the preauthorization.

In a second implementation, alone or in combination with the firstimplementation, the historical transaction corresponds to a most recenthistorical transaction of the plurality of historical transactions.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, process 600 includes, beforecausing the account transaction to be automatically executed,determining that a status of the transaction account satisfies athreshold for performing the transaction, wherein based on the status ofthe transaction account satisfying the threshold, the accounttransaction is automatically executed using resources associated withthe transaction account.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, the transaction periodexpiration is determined according to the transaction pattern.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, causing the accounttransaction to be automatically executed comprises at least one of:scheduling an execution of a transaction corresponding to the upcomingtransaction, designating a transaction corresponding to the upcomingtransaction for automatic execution, or executing, via a transactionback end system, a transaction with the merchant account thatcorresponds to the upcoming transaction.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, process 600 includes at leastone of: creating a transaction entry that corresponds to the transactionin the transaction log, sending a notification to a user device of theuser to indicate that the transaction has been executed, or sending anotification to a platform that is associated with the merchant accountto indicate that the transaction is associated with the user.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

FIG. 7 is a flow chart of an example process 700 for automatictransaction execution based on transaction log analysis. In someimplementations, one or more process blocks of FIG. 7 may be performedby a device (e.g., user device 430 and/or transaction analysis platform410). In some implementations, one or more process blocks of FIG. 7 maybe performed by another device or a group of devices separate from orincluding the device, such as a server device (e.g., server device 440),a transaction backend platform (e.g., transaction backend platform 450),and/or the like.

As shown in FIG. 7 , process 700 may include receiving apreauthorization associated with missed transaction prevention for atransaction account of a user (block 710). For example, the device(e.g., using processor 520, memory 530, storage component 540, inputcomponent 550, output component 560, communication interface 570, and/orthe like) may receive a preauthorization associated with missedtransaction prevention for a transaction account of a user, as describedabove.

As further shown in FIG. 7 , process 700 may include monitoring, basedon the preauthorization, a transaction log of the transaction account(block 720). For example, the device (e.g., using processor 520, memory530, storage component 540, input component 550, output component 560,communication interface 570, and/or the like) may monitor, based on thepreauthorization, a transaction log of the transaction account, asdescribed above.

As further shown in FIG. 7 , process 700 may include identifying atransaction pattern associated with a merchant account, wherein thetransaction pattern is identified based on a plurality of historicaltransactions identified in the transaction log being associated with themerchant account (block 730). For example, the device (e.g., usingprocessor 520, memory 530, storage component 540, input component 550,output component 560, communication interface 570, and/or the like) mayidentify a transaction pattern associated with a merchant account, asdescribed above. In some implementations, the transaction pattern isidentified based on a plurality of historical transactions identified inthe transaction log being associated with the merchant account.

As further shown in FIG. 7 , process 700 may include determining, basedon a characteristic of the merchant account, whether the merchantaccount is designated for automatic execution of transactions (block740). For example, the device (e.g., using processor 520, memory 530,storage component 540, input component 550, output component 560,communication interface 570, and/or the like) may determine, based on acharacteristic of the merchant account, whether the merchant account isdesignated for automatic execution of transactions, as described above.

As further shown in FIG. 7 , process 700 may include designating, basedon determining that the merchant account is not designated for automaticexecution of transactions, an account transaction to be automaticallyexecuted to prevent a missed transaction involving the merchant account(block 750). For example, the device (e.g., using processor 520, memory530, storage component 540, input component 550, output component 560,communication interface 570, and/or the like) may designate, based ondetermining that the merchant account is not designated for automaticexecution of transactions, an account transaction to be automaticallyexecuted to prevent a missed transaction involving the merchant account,as described above.

Process 700 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the preauthorization is specificallyassociated with the merchant account.

In a second implementation, alone or in combination with the firstimplementation, process 700 may include, when determining whether themerchant account is designated for automatic execution of transactions,determining at least one of: whether an automatic transaction setting ofthe merchant account has been activated, or whether a user hasauthorized automatic transactions between the transaction account andthe merchant account.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, the transaction account is a firsttransaction account, and process 700 may include determining that astatus of the transaction account does not satisfy a threshold forexecuting the account transaction, wherein, based on the status of thetransaction account not satisfying the threshold, the transaction isexecuted using resources associated with a second transaction accountthat is different from the first transaction account and is associatedwith the user.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, process 700 may include, beforecausing the transaction to be executed, requesting the user to authorizethe transaction, wherein the transaction is being caused to be executedbased on the user providing an authorization to perform the transaction.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, process 700 may include, whencausing the account transaction to be automatically executed, schedulinga transaction for execution based on a characteristic of the pluralityof historical transactions, or executing a transaction based on thecharacteristic of the plurality of historical transactions.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, process 700 includes sending anotification to a user device of the user to indicate that thetransaction has been executed.

Although FIG. 7 shows example blocks of process 700, in someimplementations, process 700 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 7 . Additionally, or alternatively, two or more of theblocks of process 700 may be performed in parallel.

FIG. 8 is a flow chart of an example process 800 for automatictransaction execution based on transaction log analysis. In someimplementations, one or more process blocks of FIG. 8 may be performedby a device (e.g., user device 430 and/or transaction analysis platform410). In some implementations, one or more process blocks of FIG. 8 maybe performed by another device or a group of devices separate from orincluding the device, such as a server device (e.g., server device 440),a transaction backend platform (e.g., transaction backend platform 450),and/or the like.

As shown in FIG. 8 , process 800 may include monitoring, based onreceiving a preauthorization, a transaction log of a transaction accountof a user, wherein the preauthorization is associated with missedtransaction prevention for the transaction account (block 810). Forexample, the device (e.g., using processor 520, memory 530, storagecomponent 540, input component 550, output component 560, communicationinterface 570, and/or the like) may monitor, based on receiving apreauthorization, a transaction log of a transaction account of a user,as described above. In some implementations, the preauthorization isassociated with missed transaction prevention for the transactionaccount.

As further shown in FIG. 8 , process 800 may include identifying atransaction pattern associated with a merchant account, wherein thetransaction pattern is identified based on a plurality of historicaltransactions identified in the transaction log being associated with themerchant account (block 820). For example, the device (e.g., usingprocessor 520, memory 530, storage component 540, input component 550,output component 560, communication interface 570, and/or the like) mayidentify a transaction pattern associated with a merchant account, asdescribed above. In some implementations, the transaction pattern isidentified based on a plurality of historical transactions identified inthe transaction log being associated with the merchant account.

As further shown in FIG. 8 , process 800 may include determining acharacteristic associated with an upcoming transaction that correspondsto the plurality of historical transactions (block 830). For example,the device (e.g., using processor 520, memory 530, storage component540, input component 550, output component 560, communication interface570, and/or the like) may determine a characteristic associated with anupcoming transaction that corresponds to the plurality of historicaltransactions, as described above.

As further shown in FIG. 8 , process 800 may include determining, basedon the characteristic, whether an execution of the upcoming transactionis scheduled before a transaction period expiration (block 840). Forexample, the device (e.g., using processor 520, memory 530, storagecomponent 540, input component 550, output component 560, communicationinterface 570, and/or the like) may determine, based on thecharacteristic, whether an execution of the upcoming transaction isscheduled before a transaction period expiration, as described above.

As further shown in FIG. 8 , process 800 may include designating, basedon determining that the execution of the upcoming transaction is notscheduled, an account transaction to be automatically executed beforethe transaction period expiration passes, wherein the accounttransaction corresponds to the upcoming transaction (block 850). Forexample, the device (e.g., using processor 520, memory 530, storagecomponent 540, input component 550, output component 560, communicationinterface 570, and/or the like) may designate, based on determining thatthe execution of the upcoming transaction is not scheduled, an accounttransaction to be automatically executed before the transaction periodexpiration passes, as described above. In some implementations, theaccount transaction corresponds to the upcoming transaction.

Process 800 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, the preauthorization is specificallyassociated with the merchant account.

In a second implementation, alone or in combination with the firstimplementation, the preauthorization is received in association with averification process that authenticates that the user provided thepreauthorization.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, the transaction pattern isidentified based on the plurality of historical transactions beingidentified in the transaction log as having at least one of: a samevalue, a related date of execution, or a related period of executionbetween subsequent transactions of the plurality of historicaltransactions.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, process 800 may include, whendetermining whether the execution of the upcoming transaction isscheduled, determining at least one of: whether the merchant account isassociated with engaging in automatic transactions, whether one or moreof the plurality of historical transactions are an automatictransaction, or whether a user input indicates whether the merchantaccount is associated with automatic execution.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, process 800 may include,before causing the transaction to be executed, requesting the user toauthorize the transaction, wherein the transaction is caused to beexecuted based on the user providing an authorization to perform thetransaction.

Although FIG. 8 shows example blocks of process 800, in someimplementations, process 800 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 8 . Additionally, or alternatively, two or more of theblocks of process 800 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, or the like.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, and/or thelike. A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,etc.), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

What is claimed is:
 1. A method, comprising: receiving, by a device, apreauthorization that is associated with configuring level of access foraccessing information associated with a transaction account of a user;configuring, based on the preauthorization, the device to have limitedaccess to the transaction account; monitoring, by the device and basedon the preauthorization, a transaction log of the transaction account;identifying, by the device, one or more recurring transactions,associated with a merchant account, based on a transaction period,wherein the one or more recurring transactions are identified based on aplurality of historical transactions identified in the transaction logbeing associated with the merchant account, and wherein the one or morerecurring transactions are identified based on one or more machinelearning models that utilize at least a regression algorithm;monitoring, by the device and based on access information associatedwith configuring the device to monitor online activity associated withthe user and the merchant account, the online activity; making a firstdetermination, by the device, based on the monitored online activity,and based on the one or more recurring transactions, that a settingassociated with a historical transaction of the plurality of historicaltransactions does not indicate that the historical transaction is setfor automatic processing; making a second determination, by the device,that an upcoming transaction associated with the historical transactionis not scheduled to be processed in the future; causing, by the device,based on the first determination and the second determination, anaccount transaction associated with the upcoming transaction to beautomatically executed before a transaction period associated with themerchant account expires; creating, by the device, a transaction entrythat corresponds to the transaction in the transaction log; andperforming, by the device, at least one of: sending a first notificationto another device associated with the user to indicate that thetransaction has been executed, or sending a second notification to aplatform that is associated with the merchant account to indicate thatthe transaction is associated with the user.
 2. The method of claim 1,wherein receiving the preauthorization is in association with averification process that authenticates that the user provided thepreauthorization.
 3. The method of claim 1, wherein the historicaltransaction corresponds to a recent historical transaction of theplurality of historical transactions.
 4. The method of claim 1, whereincausing the account transaction to be automatically executed furthercomprises: determining that information associated with the transactionaccount satisfies a threshold for performing the transaction; andcausing, based on the information satisfying the threshold, the accounttransaction to be automatically executed.
 5. The method of claim 1,wherein the transaction period associated with the merchant account isdetermined according to the one or more recurring transactions.
 6. Themethod of claim 1, wherein causing the account transaction to beautomatically executed further comprises at least one of: scheduling anexecution of a transaction corresponding to the upcoming transaction,designating a transaction corresponding to the upcoming transaction forautomatic execution, or executing, via a transaction back end system, atransaction that corresponds to the upcoming transaction.
 7. The methodof claim 1, wherein the preauthorization is associated with authorizingthe device to execute transactions that satisfy a threshold transactionamount, and wherein the preauthorization is associated with at least oneof: identifying merchant accounts, or transaction accounts associatedwith the user that the device may access.
 8. A device, comprising: oneor more memories; and one or more processors, coupled to the one or morememories, configured to: receive a preauthorization that is associatedwith configuring level of access for accessing information associatedwith a transaction account of a user; configure, based on thepreauthorization, the device to have limited access to the transactionaccount; monitor, based on the preauthorization, a transaction log ofthe transaction account; identify one or more recurring transactions,associated with a merchant account, based on a transaction period,wherein the one or more recurring transactions are identified based on aplurality of historical transactions identified in the transaction logbeing associated with the merchant account, and wherein the one or morerecurring transactions are identified based on one or more machinelearning models that utilize at least a regression algorithm; monitor,based on access information associated with configuring the device tomonitor online activity associated with the user and the merchantaccount, the online activity; make a first determination, based on themonitored online activity, and based on the one or more recurringtransactions, that a setting associated with a historical transaction ofthe plurality of historical transactions does not indicate that thehistorical transaction is set for automatic processing; make a seconddetermination, that an upcoming transaction associated with thehistorical transaction is not scheduled to be processed in the future;cause, based on the first determination and the second determination, anaccount transaction associated with the upcoming transaction to beautomatically executed before a transaction period associated with themerchant account expires; and create a transaction entry thatcorresponds to the transaction in the transaction log; perform at leastone of: send a first notification to another device associated with theuser to indicate that the transaction has been executed, or send asecond notification to a platform that is associated with the merchantaccount to indicate that the transaction is associated with the user. 9.The device of claim 8, wherein receiving the preauthorization is inassociation with a verification process that authenticates that the userprovided the preauthorization.
 10. The device of claim 8, wherein thehistorical transaction corresponds to a recent historical transaction ofthe plurality of historical transactions.
 11. The device of claim 8,wherein the one or more processors, to cause the account transaction tobe automatically executed, are configured to: determine that informationassociated with the transaction account satisfies a threshold forperforming the transaction; and cause, based on the informationsatisfying the threshold, the account transaction to be automaticallyexecuted.
 12. The device of claim 8, wherein the transaction periodassociated with the merchant account is determined according to the oneor more recurring transactions.
 13. The device of claim 8, wherein theone or more processors, to cause the account transaction to beautomatically executed, are configured to perform at least one of:schedule an execution of a transaction corresponding to the upcomingtransaction, designate a transaction corresponding to the upcomingtransaction for automatic execution, and execute, via a transaction backend system, a transaction that corresponds to the upcoming transaction.14. The device of claim 8, wherein the preauthorization is associatedwith authorizing the device to execute transactions that satisfy athreshold transaction amount, and wherein the preauthorization isassociated with at least one of: merchant accounts, or transactionaccounts associated with the user that the device may access.
 15. Anon-transitory computer-readable medium storing a set of instructions,the set of instructions comprising: one or more instructions that, whenexecuted by one or more processors of a device, cause the device to:receive a preauthorization that is associated with configuring level ofaccess for accessing information associated with a transaction accountof a user; configure, based on the preauthorization, the device to havelimited access to the transaction account; monitor, based on thepreauthorization, a transaction log of the transaction account; identifyone or more recurring transactions, associated with a merchant account,based on a transaction period, wherein the one or more recurringtransactions are identified based on a plurality of historicaltransactions identified in the transaction log being associated with themerchant account, and wherein the one or more recurring transactions areidentified based on one or more machine learning models that utilize atleast a regression algorithm; monitor, based on access informationassociated with configuring the device to monitor online activityassociated with the user and the merchant account, the online activity;make a first determination, based on the monitored online activity, andbased on the one or more recurring transactions, that a settingassociated with a historical transaction of the plurality of historicaltransactions does not indicate that the historical transaction is setfor automatic processing; make a second determination that an upcomingtransaction associated with the historical transaction is not scheduledto be processed in the future; cause, based on the first determinationand the second determination, an account transaction associated with theupcoming transaction to be automatically executed before a transactionperiod associated with the merchant account expires; create atransaction entry that corresponds to the transaction in the transactionlog; and perform at least one of: send a first notification to anotherdevice associated with the user to indicate that the transaction hasbeen executed, or send a second notification to a platform that isassociated with the merchant account to indicate that the transaction isassociated with the user.
 16. The non-transitory computer-readablemedium of claim 15, wherein receiving the preauthorization is inassociation with a verification process that authenticates that the userprovided the preauthorization.
 17. The non-transitory computer-readablemedium of claim 15, wherein the historical transaction corresponds to arecent historical transaction of the plurality of historicaltransactions.
 18. The non-transitory computer-readable medium of claim15, wherein the one or more instructions, that cause the device to causethe account transaction to be automatically executed, cause the deviceto: determine that information associated with the transaction accountsatisfies a threshold for performing the transaction; and cause, basedon the information satisfying the threshold, the account transaction tobe automatically executed.
 19. The non-transitory computer-readablemedium of claim 15, wherein the transaction period associated with themerchant account is determined according to the one or more recurringtransactions.
 20. The non-transitory computer-readable medium of claim15, wherein the one or more instructions, that cause the device to causethe account transaction to be automatically executed, cause the deviceto perform at least one of: schedule an execution of a transactioncorresponding to the upcoming transaction, designate a transactioncorresponding to the upcoming transaction for automatic execution, andexecute, via a transaction back end system, a transaction thatcorresponds to the upcoming transaction.